Title of Invention	METHOD FOR DERIVATION OF EIGENVECTORS FOR SPATIAL PROCESSING IN MIMO COMMUNICATION SYSTEMS
Abstract	Techniques for deriving eigenvectors based on steered reference and used for spatial processing. A steered reference is a pilot transmission on one eigenmode of a MIMO channel per symbol period using a steering vector for that eigenmode. The steered reference is used to estimate both a matrix Sigma of singular values and a matrix U of left eigenvectors of a channel response matrix H. A matrix U with orthoaonalized columns may be derived based on the estimates of Sigma and U. e.g., using QR factorization, minimum square error computation, or polar decomposition. The estimates of Sigma and U (or the estimate of Sigma and the matrix U) may be used for matched filtering of data transmission received via a first link. The estimate of U or the matrix U may also be used for spatial processing of data transmission on a second link (for reciprocal first and second links).

Title of Invention

METHOD FOR DERIVATION OF EIGENVECTORS FOR SPATIAL PROCESSING IN MIMO COMMUNICATION SYSTEMS

Abstract

Techniques for deriving eigenvectors based on steered reference and used for spatial processing. A steered reference is a pilot transmission on one eigenmode of a MIMO channel per symbol period using a steering vector for that eigenmode. The steered reference is used to estimate both a matrix Sigma of singular values and a matrix U of left eigenvectors of a channel response matrix H. A matrix U with orthoaonalized columns may be derived based on the estimates of Sigma and U. e.g., using QR factorization, minimum square error computation, or polar decomposition. The estimates of Sigma and U (or the estimate of Sigma and the matrix U) may be used for matched filtering of data transmission received via a first link. The estimate of U or the matrix U may also be used for spatial processing of data transmission on a second link (for reciprocal first and second links).

Full Text	DERIVATION OF EIGENVECTORS FOR SPATIAL PROCESSING IN MIMO COMMUNICATION SYSTEMS Claim of Priority under 35 U.S.C. §119 The present Application for Patent claims priority 10 Provisional Application No. 60/432,760 entitled "Derivation of Eigenvectors for Spatial Processing in MIMO Communication Systems" filed December 11, 2002, and assigned to the assignee hereof and hereby expressly incorporated by reference herein. BACKGROUND I. Field The present invention relates generally to data communication, and more specifically to techniques for deriving eigenvectors based or steered reference and used for spatial processing in multiple-input multiple-output (MIMO) communication systems. II. Background A MIMO system employs multiple (Nr) transmit antennas and multiple (NR) receive antennas for data transmission. A MIMO channel formed by the Nr transmit and NR receive antennas may be decomposed into Ns independent or spatial channels, where Ns dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity and/or greater reliability) if the additional dimensionalities created by the multiple transmit and receive antennas are effectively utilized. In a wireless communication system, data to be transmitted is typically processed (e.g., coded and modulated) and then up converted onto a radio frequency (RF) carrier signal to generate an RF modulated signal that is more suitable for transmission over a wireless channel. For a wireless MIMO system, up to NT RF modulated signals may be generated and transmitted simultaneously from the NT transmit antennas. The transmitted RF modulated signals may reach the NR receive antennas via a number of propagation paths in the wireless channel. The characteristics of the propagation paths typically vary over time due to various factors such as, for example, fading, multipath, and external interference. Consequently, the RF modulated signals may experience different channel conditions (e.g., different fading and multipath effects) and may be associated with different complex gains and signal-to-noise ratios ,SNRs). [0005] To achieve high performance, it is often necessary to estimate the response of the wireless channel between the transmitter and the receiver. For a MEVIO system, the channel response may be characterized by a channel response matrix H, which includes 'NTNR complex gain values for N-NR different transmit/receive antenna pairs (i.e., one complex gain for each of the NT transmit antennas and each of the NR receive antennas). Channel estimation is normally performed by transmitting a pilot (i.e., a reference signal) from the transmitter to the receiver. The pilot is typically generated based on known pilot symbols and processed in a known manner (i.e., known a priori by the receiver). The receiver can then estimate the channel gains as the ratio of the received pilot symbols over the known pilot symbols. [0006] The channel response estimate may be needed by the transmitter to perform spatial processing for data transmission. The channel response estimate may also be needed by the receiver to perform spatial processing (or matched filtering) on the received signals to recover the transmitted data. Spatial processing needs to be performed by the receiver and is typically also performed by the transmitter to utilize the Ns independent channels of the MIMO channel. [0007] For a MIMO system, a relatively large amount of system resources may be needed to transmit the pilot from the NT transmit antennas such that a sufficiently accurate estimate of the channel response can be obtained by the receiver in the presence of noise and interference. Moreover, extensive computation is normally needed to process the channel gains to obtain eigenvectors needed for spatial processing. In particular, the receiver is typically required to process the channel gains to derive a first set of eigenvectors used for spatial processing for data reception on one link and may further be required to derive a second set of eigenvectors used for spatial processing for data transmission on the other link. The derivation of the eigenvectors and the spatial processing for data transmission and reception are described below. The second set of eigenvectors typically needs to be sent back to the transmitter for its use. As can be seen, a large amount of resources may be needed to support spatial processing at the transmitter and receiver. [0008] There is therefore a need in the art for techniques :o more efficiently derive eigenvectors used for spatial processing in MIMO systems. SUMMARY [0009] Techniques are provided herein for deriving eigenvectors based on steered reference and used for spatial processing for data reception and transmission. A steered reference is a pilot transmission on only one spatial channel or eigenmode of a MEMO channel for a given symbol period, which is achieved by performing spatial processing with a steering vector for that eigenmode, as described below. The steered reference is used by a receiver to derive estimates of both a diagonal matrix ∑ of singular values and a unitary matrix U of left eigenvectors of the channel response matrix H, without having to estimate the MEMO channel response or perform singular value decomposition of H. [0012] Various aspects and embodiments of the invention are described in further detail below. BRIEF DESCRIPTION OF THE DRAWINGS [0013] The features, nature, and advantages of the present invention will become more apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout and wherein: [0014] FIG. 1 shows a flow diagram of a process for deriving an orthogonal matched alter matrix M based on a steered reference; [0015] FIG. 2 shows a wireless communication system; [0016] FIG. 3 shows a frame structure for a TDD MIMO-OFDM system; [0017] FIG. 4 shows transmission of steered reference and data on the downlink and uplink for an exemplary transmission scheme; [0018] FIG. 5 shows a block diagram of an access point and a user terminal; and [0019] FIG. 6 shows a block diagram of the spatial processing performed by the access point and user terminal for data transmission on the downlink and uplink. DETAILED DESCRIPTION [0020] The word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs. [0021] The techniques described herein for deriving eigenvectors may be used for various MIMO communication systems. For example, these techniques may be used for single-carrier MIMO systems as well as multi-carrier MIMO systems. For clarity, these techniques are described below for a single-carrier MIMO system. A unitary matrix M is characterized by the property MHM = I, which means that the columns of the unitary matrix are orthogonal to one another and the rows of the matrix are also orthogonal to one another. The columns of the matrix V are also referred to as steering vectors. Singular value decomposition is described in further detail by Gilbert Strange in a book entitled "Linear Algebra and Its Applications," Second Edition, Academic Press, 1980. [0029] As shown in equation (6), the receiver needs good estimates of the matrices 2 and U in order to perform the matched filtering to recover the data vector s. The matrices 2 and U may be obtained by transmitting a pilot from the transmitter to the receiver. The receiver can then estimate the channel response matrix H based on the received pilot and perform the singular value decomposition of this estimate, as shown in equation (3), to obtain the matrices 2 and U. However, as noted above, a large amount of resources may be needed to transmit this pilot and to perform the singular value decomposition. I. Steered Reference [0030] In an aspect, a steered reference is transmitted by the transmitter and used by the receiver to derive estimates of the-matrices 2 and U, which are needed for matched filtering. The steered reference is a pilot transmission on only one spatial channel or eigenmode for a given symbol period, which is achieved by performing spatial processing with a steering vector for that eigenmode. The receiver can then estimate the matrices 2 and U based on the steered reference, without having to estimate the MIMO channel response or perform the singular value decomposition. σm is the m-th diagonal element of the matrix 2. The receiver can thus obtain an estimate of nm σm based on the steered reference sent by the transmitter. transmitted pilot symbol p is minimized. [0037] The steered reference is transmitted for one eigenmode at a time (i.e., one eigenmode for each symbol period of steered reference transmission). The steered reference -for- all NT eigenmodes may be transmitted in various manners. In one embodiment, the steered reference is transmitted for one eigenmode for each frame, where a frame is an interval of data transmission for the system and is defined to be of a particular time duration (e.g., 2 msec). For this embodiment, the steered reference for multiple eigenmodes may be transmitted in multiple frames. In another embodiment, the steered reference is transmitted for multiple eigenmodes within one frame. This may be achieved by cycling through the NT eigenmodes in NT symbol periods. For both embodiments, the n-th steered reference symbol may be expressed as: where n is an index for either symbol period or frame number and L is the number of steered reference symbols to be transmitted. Multiple steered reference symbols may be transmitted for each eigenmode m to allow the receiver to obtain more accurate estimate [0038] The receiver is able to obtain the row vector mm for each of the NT eigenmodes based on the received steered reference for that eigenmode. The row vectors mn for all Nr eigenmodes may be used to form an initial matched filter matrix M, where [0039] The steered reference is sent for one eigenmode at a time and may be used by the receiver to obtain the matched filter vector mm for that eigenmode. Since the NT matched filter vectors mm of the matrix M are obtained individually and over different symbol periods, and due to noise and other sources of degradation in the wireless channel, the NT vectors mm of the matrix M are not likely to be orthogonal to one another. If the NT vectors mm are thereafter used for matched filtering of a received data transmission, then any errors in orthogonality among these vectors would result in cross-talk between the individual symbol streams sent on the NT eigenmodes. The cross-talk may degrade performance. II. Eigenvector Orthogonalization [0040] In another aspect, to improve performance, an enhanced matched filter matrix M is derived based on the steered reference and has row vectors that are forced to be orthogonal to one other. The orthogonalization of the row vectors of M may be achieved by various techniques such as QR factorization, minimum square error computation, and polar decomposition. All of these orthogonalization techniques are described in detail below. Other orthogonalization techniques may also be used and are within the scope of the invention. [0042] The QR factorization may be performed by various methods, including a Gram- Schmidt procedure, a householder transformation, and so on. The Gram-Schmidt procedure is recursive and may be numerically unstable. Various variants of the Gram-Schmidt procedure have been devised and are known in the art. The "classical" Gram- Schmidt procedure for orthogonalizing the matrix M is described below. The first column of RF includes one non-zero value r i.j for the first row and zeros elsewhere, where rui is the 2-norm of m1. The first column of QF is a normalized direction of the other (j-l) columns to the left of mi. The diagonal elements of RF are computed as the 2-norm of the columns of Qp (where q1 =m1,), as shown in equation (15). point in the direction of the j-l eigenvectors to the left of q. (which are associated with higher received SNRs are subtracted from m, to obtain qj The ordering also has the beneficial effect of improving the estimates of eigenvectors associated with smaller singular values. The overall result is improved performance, especially if the orthogonalized eigenvectors of QFz are-used for spatial processing for data transmission on the other link, as described below. [0052] The solution to equation (18) can be obtained from the known solution to the orthogonal Procrustes problem. This problem asks the question - given two known matrices A and B, can a unitary matrix Q be found that rotates B into A. The problem may be expressed as: minimization problem shown in equation (20). [0056] Algorithms for direct computation of polar decomposition are described by P. Zielinski and K. Zietak in "The Polar Decomposition—Properties, Applications and Algorithms," Annals of the Polish Mathematical Society. 38 (1995), and by A. A. Dubrulle in "An Optimum Iteration for the Matrix Polar Decomposition," Electronic Transactions on Numerical Analysis. Vol. 8, 1999, pp. 21-25. [0057] The solution for the optimum matched filter expressed in equation (18) may be obtained based on the solution to the orthogonal Procrustes problem described above. This may be achieved by equating M to A and ∑T to B . For the computation, an estimate of the singular values, 2. may be obtained as the 2-norm of the columns of The unitary matrix Q M that solves the minimization problem shown in equation (18) may then be given as: [0064] For the polar decomposition technique, estimates of the singular values, ∑, are orthogonal. The lack of orthogonality results in performance degradation. The orthogonalization of the matched filter matrix avoids this performance degradation. [0067] Second, QR factorization can improve the quality of the eigenvectors associated with smaller singular values. Without QR factorization, the quality of the estimates of the eigenvectors is not constant, and the estimates of the eigenvectors associated with smaller singular values are likely to be lower in quality. QR factorization can improve the quality of the eigenvectors associated with smaller singular values by rejecting certain noise components, as described above. Polar decomposition may have similar effect, but not in the direct way as QR factorization. [006S] Third, orthogonalization may reduce the amount of resources needed to transmit the steered reference. If orthogonalization is not performed, then high quality estimates of ∑ and U would be needed to ensure low cross-talk among the eigenmodes. A longer transmission period would then be needed for the steered reference for the eigenvectors associated with smaller singular values to ensure that the desired quality is obtained. High quality estimates of ∑ and U would thus require a longer period of transmission for the steered reference (which would consume more valuable system - resources) and a longer integration period for the steered reference at the receiver (which may result in longer delay for data transmission). Orthogonalization can provide the desired performance without the need for high quality estimates of I and U . III. MIMO-OFDM System [0069] The techniques for deriving eigenvectors used for spatial processing are now described for an exemplary wideband MIMO communication system that employs orthogonal frequency division multiplexing (OFDM). OFDM effectively partitions the overall system bandwidth into a number of (NF) orthogonal subbands, which are also referred to as tones, frequency bins, or frequency subchannels. With OFDM, each subband is associated with a respective subcarrier upon which data may be modulated. For a MIMO-OFDM system, each subband may be associated with multiple eigenmodes, and each eigenmode of each subband may be viewed as an independent transmission channel. [0070] For OFDM, the data or pilot to be transmitted on each usable subband is first modulated (i.e., mapped to modulation symbols) using a particular modulation scheme. One modulation symbol may be transmitted on each usable subband in each symbol period. A signal value of zero may be sent for each unused subband. For each OFDM symbol period, the modulation symbols for the usable subbands and zero signal values for the unused subbands (i.e., the modulation symbols and zeros for all NF subbands) are transformed to the time domain using an inverse fast Fourier transform (IFFT) to obtain a transformed symbol that comprises NF time-domain samples. To combat inter-symbol interference (ISI) caused by frequency selective fading, a portion of each transformed symbol is often repeated (which is often referred to as adding a cyclic prefix) to form a corresponding OFDM symbol. The OFDM symbol is then processed and transmitted over the wireless channel. An OFDM symbol period, which is also referred to as a symbol period, corresponds to the duration of one OFDM symbol. [0071] For this exemplary system, the downlink and uplink share a single frequency band using time-division duplex (TDD). For a TDD MIMO-OFDM system, the downlink and uplink channel responses may be assumed to be reciprocal of one another. [0072] FIG. 2 shows a wireless communication system 200 that includes a number of access points (APs) 210 that communicate with a number ox user terminals (UTs) 220. (For simplicity, only one access point is shown in FIG. 2.) An access point may also be referred to as a base station or some other terminology. Each user terminal may be a fixed or mobile terminal and may also be referred to as an access terminal, a mobile station, a remote station, a user equipment (UE), a wireless device, or some other terminology. Each user terminal may communicate with one or possibly multiple access points on the downlink and/or the uplink at any given moment. The downlink (i.e., forward link) refers to transmission from the access point to the user terminal, and the uplink (i.e., reverse link) refers to transmission from the user terminal to the access point. The channel response between each access point and each user terminal may be characterized by a set of channel response matrices H(k), for k € K where K represents the set of all subbands of interest (e.g., the usable subbands). [0073] In the following description for a pair of communicating access point and user Terminal, it is assumed that calibration has been performed to account for differences between the transmit and receive chains of the access point and the user terminal. The [0074] FIG. 3 shows an embodiment of a frame structure 300 that may be used for a TDD MIMO-OFDM system. Data transmission occurs in units of TDD frames, with each TDD frame covering a particular time duration (e.g., 2 msec). Each TDD frame, is partitioned into a downlink phase and an uplink phase. The downlink phase is further partitioned into multiple segments for multiple downlink transport channels. In the embodiment shown in FIG. 3, the downlink transport channels include a broadcast channel (BCH), a forward control channel (FCCH), and a forward channel (FCH). Similarly, the uplink phase is partitioned into multiple segments for multiple uplink transport channels. In the embodiment shown in FIG. 3, the uplink transport channels include a reverse channel (RCH) and a random access channel (RACH). [0075] In the downlink phase, a BCH segment 310 is used to transmit one BCH protocol data unit (PDU) 312, which includes a beacon pilot 314, a MIMO pilot 316, and a BCH message 318. The beacon pilot is transmitted from all access point antennas and is used by the user terminals for timing and frequency acquisition. The MEMO pilot is transmitted from all access point antennas with different orthogonal codes and is used by the user terminals for channel estimation. The BCH message carries system parameters for the user terminals in the system. An FCCH segment 320 is used to transmit one FCCH PDU, which carries assignments for downlink and uplink resources and other signaling for the user terminals. An FCH segment 330 is used to transmit one or more FCH PDUs 332. Different types of FCH PDU may be defined. For example, an FCH PDU 332a includes only a data packet 336a, and an FCH PDU 332b includes a downlink steered reference 334b and a data packet 336b. [0076] In the uplink phase, an RCH segment 340 is used re transmit one or more RCH PDUs 342 on the uplink. Different types of RCH PDU may also be defined. For example, an RCH PDU 342a includes an uplink steered reference 344a and a data packet 346a. An RACH segment 350 is used by the user terminals to gain access to the system and to send short messages on the uplink. An RACH PDU 352 may be sent within RACH segment 350 and includes an uplink steered reference 354 and a message 356. [0077] For the embodiment shown in FIG. 3, the beacon and MIMO pilots are sent on the downlink in the BCH segment in each TDD frame. A steered reference may or may not be sent in any given FCH/RCH PDU. A steered reference may also be sent in an RACH PDU to allow the access point to estimate pertinent vectors during system access. [0078] For simplicity, the following description is for a communication between one access point and one user terminal. The MIMO pilot is transmitted by the access point and used by the user terminal to obtain an estimate of the calibrated downlink channel [0080] As shown in equations (32) and (33), the matrices of left and right eigenvectors for one link are the complex conjugate of the matrices of right and left eigenvectors, respectively, for the other link. For simplicity, reference :o the matrices Ua (k) and [0081] The singular value decomposition may be performed independently for the estimate for the best eigenmode for subband k, which is also often referred to as the "principal" eigenmode. [0082] A "wideband" eigenmode may be defined as the set of same-order eigenmodes of all subbands after the ordering. Thus, the m-th wideband eigenmode includes the with eigenmodes of all subbands. Each wideband eigenmode is associated with a respective set of eigenvectors for all of the subbands. The "principal" wideband eigenmode is the one associated with the largest singular value estimate in the matrix [0084] The received uplink steered reference at the access point may be expressed as: eigenmode. [0085] The access point can obtain an initial matched filter matrix Map(k), for k € K , based on the uplink steered reference, as described above. The access point may uplink data transmission from the user terminal, as described below. [0090] The spatial processing performed by the user terminal to transmit data on multiple eigenmodes on the uplink may be expressed as: subband. [0092] The matched filtering by the access point may be expressed as: for k € K, for spatial processing for data transmission on the downlink to the user terminal. The spatial processing performed by the access point to transmit data on multiple eigenmodes on the downlink may be expressed as: for the downlink. Downlink data transmission can similarly occur on any number of wideband eigenmodes from 1 to Ns. [0094] The received downlink data transmission at the user terminal may be expressed as: The diagonal matrix ∑(k) is derived from the singular value decomposition shown in equation (32). [0096] Table 1 summarizes the spatial processing at the access point and user terminal for both data transmission and reception on multiple wideband eigenmodes. subband k. The subscripts "dn" and "up" for these vectors denote downlink and uplink transmissions, respectively. [0098] FIG- 4 shows transmission of steered reference and data on the downlink and uplink for an exemplary transmission scheme. The MIMO pilot is transmitted on the downlink by the access point in each TDD frame (block 412). The user terminal receives and processes the downlink MIMO pilot to obtain an estimate the downlink vectors when used for data transmission. The access point receives and processes the uplink steered reference on the RACH or the RCH to obtain the matrices ∑(k) and eigenvectors that may be used for both data reception as well as data transmission. The user terminal may thereafter transmit the uplink steered reference and data on the RCH using the matrices Vu.(k), for k € K , as shown in equation (41) and FIG. 3 (step 432). The access point receives and processes the uplink steered reference on the RCH to [00100] The access point may thereafter transmit an optional downlink steered reference and data on the FCH using the matrices V (k) ,for k€K, as shown in equation (44) and FIG. 3 (step 442). If a downlink steered reference is transmitted, then the user terminal can process the downlink steered reference to update the matrices ∑(k) and step 444). [00101] The pilot and data transmission scheme shown in FIG. 4 provides several advantages. First, the MIMO pilot transmitted by the access point may be used by for k € K, is distributed among the user terminals (i.e.. each user terminal performs singular value decomposition of its own set of estimated channel response matrices for for k € K , which are used for uplink and downlink spatial processing, based on the steered reference without having to estimate the MIMO channel response. [00102] Various other transmission schemes may also be implemented for MIMO and MIMO-OFDM systems, and this is within the scope of the invention. For example, the MIMO pilot may be transmitted by the user terminal and the steered reference may be transmitted by the access point. [00103] FIG. 5 shows a block diagram of an embodiment of an access point 210x and a user terminal 220x in MIMO-OFDM system 200. For clarity, in this embodiment, access point 210x is equipped with four antennas that can be used for data transmission and reception, and user terminal 220x is also equipped with four antennas for data transmission/reception. In general, the access point and user terminal may each be equipped with any number of transmit antennas and any number of receive antennas. [00104] On the downlink, at access point 210x, a transmit (TX) data processor 514 receives traffic data from a data source 512 and signaling and other data from a controller 530. TX data processor 514 formats, codes, interleaves, and modulates the data to provide modulation symbols, which are also referred to as data symbols. A TX spatial processor 520 then receives and multiplexes the data symbols with pilot and provides four streams of transmit symbols for the four transmit antennas. Each modulator (MOD) 522 receives and processes a respective transmit symbol stream to provide a corresponding downlink modulated signal. The four downlink modulated signals from modulators 522a through 522d are then transmitted from antennas 524a through 524d, respectively. [00105] At user terminal 220x, four antennas 552a through 552d receive the transmitted downlink modulated signals, and each antenna provides a received signal to a respective demodulator (DEMOD) 554. Each demodulator 554 performs processing complementary to that performed by modulator 522 and provides received symbols. A receive (RX) spatial processor 560 then performs matched filtering on the received symbols from all demodulators 554a through 554d to provide recovered data symbols, which are estimates of the data symbols transmitted by the access point. An RX data processor 570 further processes (e.g., symbol demaps, deinterleaves, and decodes) the recovered data symbols to provide decoded data, which may be provided to a data sink 572 for storage and/or a controller 580 for further processing. [00106] RX spatial processor 560 also processes the received pilot symbols to obtain an [00107] The processing for the uplink may be the same or different from the processing for the downlink. Data and signaling are processed (e.g., coded, interleaved, and modulated) by a TX data processor 588, multiplexed with pilot symbols, and further spatially processed by TX spatial processor 590 with the matrices Vat(k), for k € K. The transmit symbols from TX spatial processor 590 are further processed by modulators 554a through 554d to generate four uplink modulated signals, which are then transmitted via antennas 552a through 552d. [00108] At access point 510, the uplink modulated signals are received by antennas 524a through 524d and demodulated by demodulators 522a through 522d to provide received symbols for the uplink steered reference and data transmission. An RX spatial processor 540 then processes the received uplink steered reference to obtain estimates of spatial processing. [00109] RX spatial processor 540 performs matched filtering of the received uplink data further processed by an RX data processor 542 to provide decoded data. The decoded data may be provided to a data sink 544 for storage and or controller 530 for further processing. [00110] Controller 530 performs the processing to obtain the matched filter matrices operation of various processing units at the access point and user terminal, respectively. Memory units 532 and 582 store data and program codes used by controllers 530 and 580, respectively. [00111] FIG. 6 shows a block diagram of the spatial processing performed by access point 210x and user terminal 220x to transmit data on multiple eigenmodes on the downlink and uplink. [00112] On the downlink, within TX spatial processor 520 at access point 210x, the data processed by a transmit chain 614 within modulator 522 and transmitted over the MIMO channel to user terminal 220x. Unit 610 performs the spatial processing for downlink data transmission. 658 perform the downlink matched filtering. [00114] On the uplink, within TX spatial processor 590 an user terminal 220x, the data transmitted over the MIMO channel to access point 210x. Unit 660 performs the spatial processing for uplink data transmission. [00115] At access point 210x, the uplink modulated signals are processed by a receive perform the uplink matched filtering. [00116] The techniques described herein to derive eigenvectors for spatial processing may be implemented by various means. For example, these techniques may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the elements used for these techniques may be implemented within one or more application specific integrated circuits (ASICs). digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro- controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof. [00117] For a software implementation, the techniques may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit (e.g.. memory units 532 and 582 in FIG. 5) and executed by a processor (e.g.." controllers 530 and 580). The memory unit may be implemented within the processor or external to the processor, in which case it car be communicatively coupled to the processor via various means as is known in the art. [00118] Headings are included herein for reference and to aid in locating certain sections. These headings are not intended to limit the scope of the concepts described therein under, and these concepts may have applicability in other sections throughout the entire specification. [00119] The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. CLAIMS 1. In a wireless multiple-input multiple-output (MIMO) communication system, a method of deriving a matched filter based on a steered reference, comprising: obtaining a plurality of sets of received symbols for the steered reference received via a first link and generated based on a plurality of steering vectors; and deriving the matched filter based on the plurality of sets of received symbols, wherein the matched filter includes a plurality of eigenvectors corresponding to the plurality of steering vectors. 2. The method of claim 1, wherein each of the plurality of sets of received symbols is for a steered reference symbol generated based on one of the plurality of steering vectors. 3. The method of claim 1, wherein the plurality of eigenvectors of the matched filter are orthogonal to one another. 4. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using QR factorization. 5. The method of claim 4, further comprising: estimating gains associated with the plurality of steering vectors based on the plurality of sets of received symbols; and ordering the plurality of eigenvectors based on the estimated gains. 6. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using minimum square error computation. 7. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using polar decomposirion. 8. The method of claim 1, wherein the steered reference is received over multiple frames. 9. The method of claim 1, further comprising: performing matched filtering of a data transmission received via the first link using the matched filter. 10. In a wireless multiple-input multiple-output (MIMO) communication system, a method of deriving eigenvectors used for spatial processing, comprising: obtaining a plurality of sets of received symbols for a steered reference received via a first link and generated based on a plurality of steering vectors, wherein each of the plurality of sets of received symbols is for a steered reference symbol generated based on one of the plurality of steering vectors; determining a plurality of scaled vectors based on the plurality of sets of received symbols, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and deriving a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are used for matched filtering of data transmission received via the first link. 11. The method of claim 10, wherein each of the plurality of scaled vectors is determined based on at least one set of received symbols for at least one steered reference symbol generated based on the corresponding steering vector. 12. The method of claim 10, wherein the plurality of eigenvectors are orthogonal to one another. 13. The method of claim 12, wherein the deriving includes performing QR factorization on the plurality of scaled vectors to obtain the plurality of eigenvectors. 14. The method of claim 12, wherein the deriving includes performing polar decomposition on the plurality of scaled vectors to obtain the plurality of eigenvectors. 15. The method of claim 12, wherein the deriving includes performing minimum square error computation on the plurality of scaled vectors to obtain the plurality of eigenvectors. 16. The method of claim 12, further comprising: estimating singular values based on the plurality of scaled vectors; and deriving a matched filter for the first link based on the plurality of eigenvectors and the estimated singular values. 17. The method of claim 12, wherein the plurality of eigenvectors are used for spatial processing for data transmission on a second link. 18. The method of claim 17, wherein the first link is an uplink and the second link is a downlink in the MIMO communication system. 19. The method of claim 12, wherein the MIMO communication system utilizes orthogonal frequency division multiplexing (OFDM), and wherein the plurality of eigenvectors are derived for each of a plurality of subbands. 20. A memory communicatively coupled to a digital signal processing device (DSPD) capable of interpreting digital information to: determine a plurality of scaled vectors based on a plurality of sets of received symbols for a steered reference generated based on a plurality of steering vectors and received via a first link in a wireless multiple-input multiple-output (MIMO) communication system, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and derive a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are suitable for use for spatial processing. 21. An apparatus in a wireless multiple-input multiple-output (MIMO) communication system, comprising: a receive spatial processor operative to process a plurality of sets of received symbols for a steered reference to provide a plurality of scaled vectors, wherein the steered reference is received via a first link and generated based on a plurality of steering vectors, and wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and a controller operative to derive a plurality of eigenvectors based on the plurality of scaled vectors, and wherein the receive spatial processor is further operative to perform matched filtering of a first data transmission received via the first link using the plurality of eigenvectors. 22. The apparatus of claim 21, wherein the controller is further operative to estimate singular values based on the plurality of scaled vectors and to derive a matched filter for the first link based on the plurality of eigenvectors and the estimated singular values. 23. The apparatus of claim 21, wherein the plurality of eigenvectors are orthogonal to one another. 24. The apparatus of claim 23, wherein the controller is operative to perform QR factorization, polar decomposition, or minimum square error computation on the plurality of scaled vectors to obtain the plurality of eigenvectors. 25. The apparatus of claim 21, further comprising: a TX spatial processor operative to perform spatial processing for a second data transmission on a second link using the plurality of eigenvectors. 26. The apparatus of claim 21, wherein the MIMO communication system utilizes orthogonal frequency division multiplexing (OFDM), and wherein the plurality of eigenvectors are derived for each of a plurality of subbands. 27. An apparatus in a wireless multiple-input multiple-output (MIMO) communication system, comprising: means for determining a plurality of scaled vectors based on a plurality of sets of received symbols for a steered reference received via a first link and generated based on a plurality of steering vectors, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and means for deriving a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are suitable for use for spatial processing. 28. The apparatus of claim 27, further comprising: means for performing matched filtering of a first data transmission received via the first link using the plurality of eigenvectors. 29. The apparatus of claim 27, further comprising: means for performing spatial processing for a second data transmission on a second link using the plurality of eigenvectors. 30. The apparatus of claim 27, wherein the plurality of eigenvectors are orthogonal to one another.

Full Text

DERIVATION OF EIGENVECTORS FOR SPATIAL PROCESSING IN MIMO COMMUNICATION SYSTEMS
Claim of Priority under 35 U.S.C. §119
The present Application for Patent claims priority 10 Provisional Application No. 60/432,760 entitled "Derivation of Eigenvectors for Spatial Processing in MIMO Communication Systems" filed December 11, 2002, and assigned to the assignee hereof and hereby expressly incorporated by reference herein.
BACKGROUND
I. Field
The present invention relates generally to data communication, and more specifically to techniques for deriving eigenvectors based or steered reference and used for spatial processing in multiple-input multiple-output (MIMO) communication systems.
II. Background
A MIMO system employs multiple (Nr) transmit antennas and multiple (NR) receive antennas for data transmission. A MIMO channel formed by the Nr transmit and NR receive antennas may be decomposed into Ns independent or spatial channels, where Ns dimension. The MIMO system can provide improved performance (e.g., increased transmission capacity and/or greater reliability) if the additional dimensionalities created by the multiple transmit and receive antennas are effectively utilized.
In a wireless communication system, data to be transmitted is typically processed (e.g., coded and modulated) and then up converted onto a radio frequency (RF) carrier signal to generate an RF modulated signal that is more suitable for transmission over a wireless channel. For a wireless MIMO system, up to NT RF modulated signals may be generated and transmitted simultaneously from the NT transmit antennas. The transmitted RF modulated signals may reach the NR receive antennas via a number of propagation paths in the wireless channel. The characteristics of the propagation paths typically vary over time due to various factors such as, for

example, fading, multipath, and external interference. Consequently, the RF modulated
signals may experience different channel conditions (e.g., different fading and multipath
effects) and may be associated with different complex gains and signal-to-noise ratios
,SNRs).
[0005] To achieve high performance, it is often necessary to estimate the response of
the wireless channel between the transmitter and the receiver. For a MEVIO system, the channel response may be characterized by a channel response matrix H, which includes
'NTNR complex gain values for N-NR different transmit/receive antenna pairs (i.e., one
complex gain for each of the NT transmit antennas and each of the NR receive antennas). Channel estimation is normally performed by transmitting a pilot (i.e., a reference signal) from the transmitter to the receiver. The pilot is typically generated based on known pilot symbols and processed in a known manner (i.e., known a priori by the receiver). The receiver can then estimate the channel gains as the ratio of the received pilot symbols over the known pilot symbols.
[0006] The channel response estimate may be needed by the transmitter to perform
spatial processing for data transmission. The channel response estimate may also be needed by the receiver to perform spatial processing (or matched filtering) on the received signals to recover the transmitted data. Spatial processing needs to be performed by the receiver and is typically also performed by the transmitter to utilize the Ns independent channels of the MIMO channel.
[0007] For a MIMO system, a relatively large amount of system resources may be
needed to transmit the pilot from the NT transmit antennas such that a sufficiently accurate estimate of the channel response can be obtained by the receiver in the presence of noise and interference. Moreover, extensive computation is normally needed to process the channel gains to obtain eigenvectors needed for spatial processing. In particular, the receiver is typically required to process the channel gains to derive a first set of eigenvectors used for spatial processing for data reception on one link and may further be required to derive a second set of eigenvectors used for spatial processing for data transmission on the other link. The derivation of the eigenvectors and the spatial processing for data transmission and reception are described below. The second set of eigenvectors typically needs to be sent back to the transmitter for its use.

As can be seen, a large amount of resources may be needed to support spatial processing
at the transmitter and receiver.
[0008] There is therefore a need in the art for techniques :o more efficiently derive
eigenvectors used for spatial processing in MIMO systems.
SUMMARY
[0009] Techniques are provided herein for deriving eigenvectors based on steered
reference and used for spatial processing for data reception and transmission. A steered reference is a pilot transmission on only one spatial channel or eigenmode of a MEMO channel for a given symbol period, which is achieved by performing spatial processing with a steering vector for that eigenmode, as described below. The steered reference is used by a receiver to derive estimates of both a diagonal matrix ∑ of singular values and a unitary matrix U of left eigenvectors of the channel response matrix H, without having to estimate the MEMO channel response or perform singular value decomposition of H.

[0012] Various aspects and embodiments of the invention are described in further detail
below.

BRIEF DESCRIPTION OF THE DRAWINGS
[0013] The features, nature, and advantages of the present invention will become more
apparent from the detailed description set forth below when taken in conjunction with the drawings in which like reference characters identify correspondingly throughout and wherein:
[0014] FIG. 1 shows a flow diagram of a process for deriving an orthogonal matched
alter matrix M based on a steered reference;
[0015] FIG. 2 shows a wireless communication system;
[0016] FIG. 3 shows a frame structure for a TDD MIMO-OFDM system;
[0017] FIG. 4 shows transmission of steered reference and data on the downlink and
uplink for an exemplary transmission scheme;
[0018] FIG. 5 shows a block diagram of an access point and a user terminal; and
[0019] FIG. 6 shows a block diagram of the spatial processing performed by the access
point and user terminal for data transmission on the downlink and uplink.
DETAILED DESCRIPTION
[0020] The word "exemplary" is used herein to mean "serving as an example, instance,
or illustration." Any embodiment or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments or designs.
[0021] The techniques described herein for deriving eigenvectors may be used for
various MIMO communication systems. For example, these techniques may be used for single-carrier MIMO systems as well as multi-carrier MIMO systems. For clarity, these techniques are described below for a single-carrier MIMO system.

A unitary matrix M is characterized by the property MHM = I, which means that the columns of the unitary matrix are orthogonal to one another and the rows of the matrix are also orthogonal to one another. The columns of the matrix V are also referred to as steering vectors. Singular value decomposition is described in further detail by Gilbert Strange in a book entitled "Linear Algebra and Its Applications," Second Edition, Academic Press, 1980.

[0029] As shown in equation (6), the receiver needs good estimates of the matrices 2
and U in order to perform the matched filtering to recover the data vector s. The matrices 2 and U may be obtained by transmitting a pilot from the transmitter to the receiver. The receiver can then estimate the channel response matrix H based on the received pilot and perform the singular value decomposition of this estimate, as shown in equation (3), to obtain the matrices 2 and U. However, as noted above, a large amount of resources may be needed to transmit this pilot and to perform the singular value decomposition.
I. Steered Reference
[0030] In an aspect, a steered reference is transmitted by the transmitter and used by the
receiver to derive estimates of the-matrices 2 and U, which are needed for matched
filtering. The steered reference is a pilot transmission on only one spatial channel or eigenmode for a given symbol period, which is achieved by performing spatial processing with a steering vector for that eigenmode. The receiver can then estimate the matrices 2 and U based on the steered reference, without having to estimate the MIMO channel response or perform the singular value decomposition.

σm is the m-th diagonal element of the matrix 2. The receiver can thus obtain an estimate of nm σm based on the steered reference sent by the transmitter.

transmitted pilot symbol p is minimized.
[0037] The steered reference is transmitted for one eigenmode at a time (i.e., one
eigenmode for each symbol period of steered reference transmission). The steered reference -for- all NT eigenmodes may be transmitted in various manners. In one embodiment, the steered reference is transmitted for one eigenmode for each frame,

where a frame is an interval of data transmission for the system and is defined to be of a particular time duration (e.g., 2 msec). For this embodiment, the steered reference for multiple eigenmodes may be transmitted in multiple frames. In another embodiment, the steered reference is transmitted for multiple eigenmodes within one frame. This may be achieved by cycling through the NT eigenmodes in NT symbol periods. For both embodiments, the n-th steered reference symbol may be expressed as:

where n is an index for either symbol period or frame number and L is the number of steered reference symbols to be transmitted. Multiple steered reference symbols may be transmitted for each eigenmode m to allow the receiver to obtain more accurate estimate
[0038] The receiver is able to obtain the row vector mm for each of the NT eigenmodes
based on the received steered reference for that eigenmode. The row vectors mn for all
Nr eigenmodes may be used to form an initial matched filter matrix M, where

[0039] The steered reference is sent for one eigenmode at a time and may be used by
the receiver to obtain the matched filter vector mm for that eigenmode. Since the NT
matched filter vectors mm of the matrix M are obtained individually and over different symbol periods, and due to noise and other sources of degradation in the wireless channel, the NT vectors mm of the matrix M are not likely to be orthogonal to one another. If the NT vectors mm are thereafter used for matched filtering of a received data transmission, then any errors in orthogonality among these vectors would result in cross-talk between the individual symbol streams sent on the NT eigenmodes. The cross-talk may degrade performance.
II. Eigenvector Orthogonalization
[0040] In another aspect, to improve performance, an enhanced matched filter matrix
M is derived based on the steered reference and has row vectors that are forced to be

orthogonal to one other. The orthogonalization of the row vectors of M may be achieved by various techniques such as QR factorization, minimum square error computation, and polar decomposition. All of these orthogonalization techniques are described in detail below. Other orthogonalization techniques may also be used and are within the scope of the invention.

[0042] The QR factorization may be performed by various methods, including a Gram-
Schmidt procedure, a householder transformation, and so on. The Gram-Schmidt procedure is recursive and may be numerically unstable. Various variants of the Gram-Schmidt procedure have been devised and are known in the art. The "classical" Gram-
Schmidt procedure for orthogonalizing the matrix M is described below.

The first column of RF includes one non-zero value r i.j for the first row and zeros elsewhere, where rui is the 2-norm of m1. The first column of QF is a normalized

direction of the other (j-l) columns to the left of mi. The diagonal elements of RF
are computed as the 2-norm of the columns of Qp (where q1 =m1,), as shown in equation (15).

point in the direction of the j-l eigenvectors to the left of q. (which are associated with higher received SNRs are subtracted from m, to obtain qj The ordering also
has the beneficial effect of improving the estimates of eigenvectors associated with smaller singular values. The overall result is improved performance, especially if the
orthogonalized eigenvectors of QFz are-used for spatial processing for data transmission on the other link, as described below.

[0052] The solution to equation (18) can be obtained from the known solution to the
orthogonal Procrustes problem. This problem asks the question - given two known matrices A and B, can a unitary matrix Q be found that rotates B into A. The
problem may be expressed as:

minimization problem shown in equation (20).

[0056] Algorithms for direct computation of polar decomposition are described by P.
Zielinski and K. Zietak in "The Polar Decomposition—Properties, Applications and
Algorithms," Annals of the Polish Mathematical Society. 38 (1995), and by A. A.
Dubrulle in "An Optimum Iteration for the Matrix Polar Decomposition," Electronic
Transactions on Numerical Analysis. Vol. 8, 1999, pp. 21-25.
[0057] The solution for the optimum matched filter expressed in equation (18) may be
obtained based on the solution to the orthogonal Procrustes problem described above.
This may be achieved by equating M to A and ∑T to B . For the computation, an estimate of the singular values, 2. may be obtained as the 2-norm of the columns of

The unitary matrix Q M that solves the minimization problem shown in equation (18) may then be given as:

[0064] For the polar decomposition technique, estimates of the singular values, ∑, are

orthogonal. The lack of orthogonality results in performance degradation. The orthogonalization of the matched filter matrix avoids this performance degradation.
[0067] Second, QR factorization can improve the quality of the eigenvectors associated
with smaller singular values. Without QR factorization, the quality of the estimates of the eigenvectors is not constant, and the estimates of the eigenvectors associated with smaller singular values are likely to be lower in quality. QR factorization can improve the quality of the eigenvectors associated with smaller singular values by rejecting certain noise components, as described above. Polar decomposition may have similar effect, but not in the direct way as QR factorization.
[006S] Third, orthogonalization may reduce the amount of resources needed to transmit
the steered reference. If orthogonalization is not performed, then high quality estimates of ∑ and U would be needed to ensure low cross-talk among the eigenmodes. A
longer transmission period would then be needed for the steered reference for the eigenvectors associated with smaller singular values to ensure that the desired quality is obtained. High quality estimates of ∑ and U would thus require a longer period of transmission for the steered reference (which would consume more valuable system - resources) and a longer integration period for the steered reference at the receiver

(which may result in longer delay for data transmission). Orthogonalization can provide the desired performance without the need for high quality estimates of I and U .
III. MIMO-OFDM System
[0069] The techniques for deriving eigenvectors used for spatial processing are now
described for an exemplary wideband MIMO communication system that employs orthogonal frequency division multiplexing (OFDM). OFDM effectively partitions the overall system bandwidth into a number of (NF) orthogonal subbands, which are also referred to as tones, frequency bins, or frequency subchannels. With OFDM, each subband is associated with a respective subcarrier upon which data may be modulated. For a MIMO-OFDM system, each subband may be associated with multiple eigenmodes, and each eigenmode of each subband may be viewed as an independent transmission channel.
[0070] For OFDM, the data or pilot to be transmitted on each usable subband is first
modulated (i.e., mapped to modulation symbols) using a particular modulation scheme. One modulation symbol may be transmitted on each usable subband in each symbol period. A signal value of zero may be sent for each unused subband. For each OFDM symbol period, the modulation symbols for the usable subbands and zero signal values for the unused subbands (i.e., the modulation symbols and zeros for all NF subbands) are transformed to the time domain using an inverse fast Fourier transform (IFFT) to obtain a transformed symbol that comprises NF time-domain samples. To combat inter-symbol interference (ISI) caused by frequency selective fading, a portion of each transformed symbol is often repeated (which is often referred to as adding a cyclic prefix) to form a corresponding OFDM symbol. The OFDM symbol is then processed and transmitted over the wireless channel. An OFDM symbol period, which is also referred to as a symbol period, corresponds to the duration of one OFDM symbol.
[0071] For this exemplary system, the downlink and uplink share a single frequency
band using time-division duplex (TDD). For a TDD MIMO-OFDM system, the downlink and uplink channel responses may be assumed to be reciprocal of one another.

[0072] FIG. 2 shows a wireless communication system 200 that includes a number of
access points (APs) 210 that communicate with a number ox user terminals (UTs) 220. (For simplicity, only one access point is shown in FIG. 2.) An access point may also be referred to as a base station or some other terminology. Each user terminal may be a fixed or mobile terminal and may also be referred to as an access terminal, a mobile station, a remote station, a user equipment (UE), a wireless device, or some other terminology. Each user terminal may communicate with one or possibly multiple access points on the downlink and/or the uplink at any given moment. The downlink (i.e., forward link) refers to transmission from the access point to the user terminal, and the uplink (i.e., reverse link) refers to transmission from the user terminal to the access point. The channel response between each access point and each user terminal may be characterized by a set of channel response matrices H(k), for k € K where K
represents the set of all subbands of interest (e.g., the usable subbands).
[0073] In the following description for a pair of communicating access point and user
Terminal, it is assumed that calibration has been performed to account for differences between the transmit and receive chains of the access point and the user terminal. The

[0074] FIG. 3 shows an embodiment of a frame structure 300 that may be used for a
TDD MIMO-OFDM system. Data transmission occurs in units of TDD frames, with each TDD frame covering a particular time duration (e.g., 2 msec). Each TDD frame, is partitioned into a downlink phase and an uplink phase. The downlink phase is further partitioned into multiple segments for multiple downlink transport channels. In the embodiment shown in FIG. 3, the downlink transport channels include a broadcast channel (BCH), a forward control channel (FCCH), and a forward channel (FCH). Similarly, the uplink phase is partitioned into multiple segments for multiple uplink transport channels. In the embodiment shown in FIG. 3, the uplink transport channels include a reverse channel (RCH) and a random access channel (RACH).
[0075] In the downlink phase, a BCH segment 310 is used to transmit one BCH
protocol data unit (PDU) 312, which includes a beacon pilot 314, a MIMO pilot 316, and a BCH message 318. The beacon pilot is transmitted from all access point antennas and is used by the user terminals for timing and frequency acquisition. The MEMO pilot is transmitted from all access point antennas with different orthogonal codes and is used by the user terminals for channel estimation. The BCH message carries system parameters for the user terminals in the system. An FCCH segment 320 is used to transmit one FCCH PDU, which carries assignments for downlink and uplink resources and other signaling for the user terminals. An FCH segment 330 is used to transmit one or more FCH PDUs 332. Different types of FCH PDU may be defined. For example, an FCH PDU 332a includes only a data packet 336a, and an FCH PDU 332b includes a downlink steered reference 334b and a data packet 336b.

[0076] In the uplink phase, an RCH segment 340 is used re transmit one or more RCH
PDUs 342 on the uplink. Different types of RCH PDU may also be defined. For example, an RCH PDU 342a includes an uplink steered reference 344a and a data packet 346a. An RACH segment 350 is used by the user terminals to gain access to the system and to send short messages on the uplink. An RACH PDU 352 may be sent within RACH segment 350 and includes an uplink steered reference 354 and a message 356.
[0077] For the embodiment shown in FIG. 3, the beacon and MIMO pilots are sent on
the downlink in the BCH segment in each TDD frame. A steered reference may or may not be sent in any given FCH/RCH PDU. A steered reference may also be sent in an RACH PDU to allow the access point to estimate pertinent vectors during system access.
[0078] For simplicity, the following description is for a communication between one
access point and one user terminal. The MIMO pilot is transmitted by the access point and used by the user terminal to obtain an estimate of the calibrated downlink channel

[0080] As shown in equations (32) and (33), the matrices of left and right eigenvectors
for one link are the complex conjugate of the matrices of right and left eigenvectors,
respectively, for the other link. For simplicity, reference :o the matrices Ua (k) and

[0081] The singular value decomposition may be performed independently for the

estimate for the best eigenmode for subband k, which is also often referred to as the
"principal" eigenmode.
[0082] A "wideband" eigenmode may be defined as the set of same-order eigenmodes
of all subbands after the ordering. Thus, the m-th wideband eigenmode includes the with eigenmodes of all subbands. Each wideband eigenmode is associated with a respective set of eigenvectors for all of the subbands. The "principal" wideband eigenmode is the one associated with the largest singular value estimate in the matrix

[0084] The received uplink steered reference at the access point may be expressed as:

eigenmode.
[0085] The access point can obtain an initial matched filter matrix Map(k), for k € K ,
based on the uplink steered reference, as described above. The access point may

uplink data transmission from the user terminal, as described below.
[0090] The spatial processing performed by the user terminal to transmit data on
multiple eigenmodes on the uplink may be expressed as:

subband.
[0092] The matched filtering by the access point may be expressed as:

for k € K, for spatial processing for data transmission on the downlink to the user terminal. The spatial processing performed by the access point to transmit data on multiple eigenmodes on the downlink may be expressed as:

for the downlink. Downlink data transmission can similarly occur on any number of
wideband eigenmodes from 1 to Ns.
[0094] The received downlink data transmission at the user terminal may be expressed
as:

The diagonal matrix ∑(k) is derived from the singular value decomposition shown in
equation (32).
[0096] Table 1 summarizes the spatial processing at the access point and user terminal
for both data transmission and reception on multiple wideband eigenmodes.

subband k. The subscripts "dn" and "up" for these vectors denote downlink and uplink transmissions, respectively.

[0098] FIG- 4 shows transmission of steered reference and data on the downlink and
uplink for an exemplary transmission scheme. The MIMO pilot is transmitted on the downlink by the access point in each TDD frame (block 412). The user terminal receives and processes the downlink MIMO pilot to obtain an estimate the downlink

vectors when used for data transmission. The access point receives and processes the uplink steered reference on the RACH or the RCH to obtain the matrices ∑(k) and

eigenvectors that may be used for both data reception as well as data transmission. The user terminal may thereafter transmit the uplink steered reference and data on the RCH
using the matrices Vu.(k), for k € K , as shown in equation (41) and FIG. 3 (step 432).
The access point receives and processes the uplink steered reference on the RCH to

[00100] The access point may thereafter transmit an optional downlink steered reference
and data on the FCH using the matrices V (k) ,for k€K, as shown in equation (44) and FIG. 3 (step 442). If a downlink steered reference is transmitted, then the user terminal can process the downlink steered reference to update the matrices ∑(k) and

step 444).
[00101] The pilot and data transmission scheme shown in FIG. 4 provides several
advantages. First, the MIMO pilot transmitted by the access point may be used by

for k € K, is distributed among the user terminals (i.e.. each user terminal performs singular value decomposition of its own set of estimated channel response matrices for

for k € K , which are used for uplink and downlink spatial processing, based on the steered reference without having to estimate the MIMO channel response.
[00102] Various other transmission schemes may also be implemented for MIMO and
MIMO-OFDM systems, and this is within the scope of the invention. For example, the MIMO pilot may be transmitted by the user terminal and the steered reference may be transmitted by the access point.
[00103] FIG. 5 shows a block diagram of an embodiment of an access point 210x and a
user terminal 220x in MIMO-OFDM system 200. For clarity, in this embodiment, access point 210x is equipped with four antennas that can be used for data transmission and reception, and user terminal 220x is also equipped with four antennas for data transmission/reception. In general, the access point and user terminal may each be equipped with any number of transmit antennas and any number of receive antennas.
[00104] On the downlink, at access point 210x, a transmit (TX) data processor 514
receives traffic data from a data source 512 and signaling and other data from a controller 530. TX data processor 514 formats, codes, interleaves, and modulates the data to provide modulation symbols, which are also referred to as data symbols. A TX spatial processor 520 then receives and multiplexes the data symbols with pilot

and provides four streams of transmit symbols for the four transmit antennas. Each
modulator (MOD) 522 receives and processes a respective transmit symbol stream to
provide a corresponding downlink modulated signal. The four downlink modulated
signals from modulators 522a through 522d are then transmitted from antennas 524a
through 524d, respectively.
[00105] At user terminal 220x, four antennas 552a through 552d receive the transmitted
downlink modulated signals, and each antenna provides a received signal to a respective demodulator (DEMOD) 554. Each demodulator 554 performs processing complementary to that performed by modulator 522 and provides received symbols. A receive (RX) spatial processor 560 then performs matched filtering on the received

symbols from all demodulators 554a through 554d to provide recovered data symbols,
which are estimates of the data symbols transmitted by the access point. An RX data
processor 570 further processes (e.g., symbol demaps, deinterleaves, and decodes) the
recovered data symbols to provide decoded data, which may be provided to a data sink
572 for storage and/or a controller 580 for further processing.
[00106] RX spatial processor 560 also processes the received pilot symbols to obtain an

[00107] The processing for the uplink may be the same or different from the processing
for the downlink. Data and signaling are processed (e.g., coded, interleaved, and modulated) by a TX data processor 588, multiplexed with pilot symbols, and further
spatially processed by TX spatial processor 590 with the matrices Vat(k), for k € K.
The transmit symbols from TX spatial processor 590 are further processed by
modulators 554a through 554d to generate four uplink modulated signals, which are
then transmitted via antennas 552a through 552d.
[00108] At access point 510, the uplink modulated signals are received by antennas 524a
through 524d and demodulated by demodulators 522a through 522d to provide received symbols for the uplink steered reference and data transmission. An RX spatial processor 540 then processes the received uplink steered reference to obtain estimates of

spatial processing.
[00109] RX spatial processor 540 performs matched filtering of the received uplink data

further processed by an RX data processor 542 to provide decoded data. The decoded
data may be provided to a data sink 544 for storage and or controller 530 for further
processing.
[00110] Controller 530 performs the processing to obtain the matched filter matrices

operation of various processing units at the access point and user terminal, respectively.
Memory units 532 and 582 store data and program codes used by controllers 530 and
580, respectively.
[00111] FIG. 6 shows a block diagram of the spatial processing performed by access
point 210x and user terminal 220x to transmit data on multiple eigenmodes on the
downlink and uplink.
[00112] On the downlink, within TX spatial processor 520 at access point 210x, the data

processed by a transmit chain 614 within modulator 522 and transmitted over the MIMO channel to user terminal 220x. Unit 610 performs the spatial processing for downlink data transmission.

658 perform the downlink matched filtering.
[00114] On the uplink, within TX spatial processor 590 an user terminal 220x, the data

transmitted over the MIMO channel to access point 210x. Unit 660 performs the spatial
processing for uplink data transmission.
[00115] At access point 210x, the uplink modulated signals are processed by a receive

perform the uplink matched filtering.
[00116] The techniques described herein to derive eigenvectors for spatial processing
may be implemented by various means. For example, these techniques may be implemented in hardware, software, or a combination thereof. For a hardware implementation, the elements used for these techniques may be implemented within one or more application specific integrated circuits (ASICs). digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, micro-

controllers, microprocessors, other electronic units designed to perform the functions described herein, or a combination thereof.
[00117] For a software implementation, the techniques may be implemented with
modules (e.g., procedures, functions, and so on) that perform the functions described herein. The software codes may be stored in a memory unit (e.g.. memory units 532 and 582 in FIG. 5) and executed by a processor (e.g.." controllers 530 and 580). The memory unit may be implemented within the processor or external to the processor, in which case it car be communicatively coupled to the processor via various means as is known in the art.
[00118] Headings are included herein for reference and to aid in locating certain
sections. These headings are not intended to limit the scope of the concepts described therein under, and these concepts may have applicability in other sections throughout the entire specification.
[00119] The previous description of the disclosed embodiments is provided to enable any
person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

CLAIMS
1. In a wireless multiple-input multiple-output (MIMO) communication
system, a method of deriving a matched filter based on a steered reference, comprising:
obtaining a plurality of sets of received symbols for the steered reference received via a first link and generated based on a plurality of steering vectors; and
deriving the matched filter based on the plurality of sets of received symbols, wherein the matched filter includes a plurality of eigenvectors corresponding to the plurality of steering vectors.
2. The method of claim 1, wherein each of the plurality of sets of received symbols is for a steered reference symbol generated based on one of the plurality of steering vectors.
3. The method of claim 1, wherein the plurality of eigenvectors of the matched filter are orthogonal to one another.
4. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using QR factorization.
5. The method of claim 4, further comprising:
estimating gains associated with the plurality of steering vectors based on the plurality of sets of received symbols; and
ordering the plurality of eigenvectors based on the estimated gains.
6. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using minimum square error computation.
7. The method of claim 3, wherein the plurality of eigenvectors of the matched filter are orthogonalized using polar decomposirion.
8. The method of claim 1, wherein the steered reference is received over multiple frames.

9. The method of claim 1, further comprising:
performing matched filtering of a data transmission received via the first link using the matched filter.
10. In a wireless multiple-input multiple-output (MIMO) communication
system, a method of deriving eigenvectors used for spatial processing, comprising:
obtaining a plurality of sets of received symbols for a steered reference received via a first link and generated based on a plurality of steering vectors, wherein each of the plurality of sets of received symbols is for a steered reference symbol generated based on one of the plurality of steering vectors;
determining a plurality of scaled vectors based on the plurality of sets of received symbols, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and
deriving a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are used for matched filtering of data transmission received via the first link.
11. The method of claim 10, wherein each of the plurality of scaled vectors is determined based on at least one set of received symbols for at least one steered reference symbol generated based on the corresponding steering vector.
12. The method of claim 10, wherein the plurality of eigenvectors are orthogonal to one another.
13. The method of claim 12, wherein the deriving includes
performing QR factorization on the plurality of scaled vectors to obtain the plurality of eigenvectors.
14. The method of claim 12, wherein the deriving includes
performing polar decomposition on the plurality of scaled vectors to obtain the plurality of eigenvectors.

15. The method of claim 12, wherein the deriving includes
performing minimum square error computation on the plurality of scaled vectors to obtain the plurality of eigenvectors.
16. The method of claim 12, further comprising:
estimating singular values based on the plurality of scaled vectors; and deriving a matched filter for the first link based on the plurality of eigenvectors and the estimated singular values.
17. The method of claim 12, wherein the plurality of eigenvectors are used for spatial processing for data transmission on a second link.
18. The method of claim 17, wherein the first link is an uplink and the second link is a downlink in the MIMO communication system.
19. The method of claim 12, wherein the MIMO communication system utilizes orthogonal frequency division multiplexing (OFDM), and wherein the plurality of eigenvectors are derived for each of a plurality of subbands.
20. A memory communicatively coupled to a digital signal processing device (DSPD) capable of interpreting digital information to:
determine a plurality of scaled vectors based on a plurality of sets of received symbols for a steered reference generated based on a plurality of steering vectors and received via a first link in a wireless multiple-input multiple-output (MIMO) communication system, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and
derive a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are suitable for use for spatial processing.
21. An apparatus in a wireless multiple-input multiple-output (MIMO)
communication system, comprising:

a receive spatial processor operative to process a plurality of sets of received symbols for a steered reference to provide a plurality of scaled vectors, wherein the steered reference is received via a first link and generated based on a plurality of steering vectors, and wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and
a controller operative to derive a plurality of eigenvectors based on the plurality of scaled vectors, and
wherein the receive spatial processor is further operative to perform matched filtering of a first data transmission received via the first link using the plurality of eigenvectors.
22. The apparatus of claim 21, wherein the controller is further operative to estimate singular values based on the plurality of scaled vectors and to derive a matched filter for the first link based on the plurality of eigenvectors and the estimated singular values.
23. The apparatus of claim 21, wherein the plurality of eigenvectors are orthogonal to one another.
24. The apparatus of claim 23, wherein the controller is operative to perform QR factorization, polar decomposition, or minimum square error computation on the plurality of scaled vectors to obtain the plurality of eigenvectors.
25. The apparatus of claim 21, further comprising:
a TX spatial processor operative to perform spatial processing for a second data transmission on a second link using the plurality of eigenvectors.
26. The apparatus of claim 21, wherein the MIMO communication system
utilizes orthogonal frequency division multiplexing (OFDM), and wherein the plurality
of eigenvectors are derived for each of a plurality of subbands.

27. An apparatus in a wireless multiple-input multiple-output (MIMO)
communication system, comprising:
means for determining a plurality of scaled vectors based on a plurality of sets of received symbols for a steered reference received via a first link and generated based on a plurality of steering vectors, wherein each of the plurality of scaled vectors corresponds to a respective one of the plurality of steering vectors; and
means for deriving a plurality of eigenvectors based on the plurality of scaled vectors, wherein the plurality of eigenvectors are suitable for use for spatial processing.
28. The apparatus of claim 27, further comprising:
means for performing matched filtering of a first data transmission received via the first link using the plurality of eigenvectors.
29. The apparatus of claim 27, further comprising:
means for performing spatial processing for a second data transmission on a second link using the plurality of eigenvectors.
30. The apparatus of claim 27, wherein the plurality of eigenvectors are
orthogonal to one another.

Documents:

1211-CHENP-2005 ABSTRACT.pdf

1211-CHENP-2005 CLAIMS.pdf

1211-CHENP-2005 CORRESPONDENCE OTHERS.pdf

1211-CHENP-2005 CORRESPONDENCE PO.pdf

1211-CHENP-2005 FORM 2.pdf

1211-CHENP-2005 FORM 3.pdf

1211-CHENP-2005 PETITIONS.pdf

1211-chenp-2005-abstract.pdf

1211-chenp-2005-assignement.pdf

1211-chenp-2005-claims.pdf

1211-chenp-2005-correspondnece-others.pdf

1211-chenp-2005-correspondnece-po.pdf

1211-chenp-2005-description(complete).pdf

1211-chenp-2005-drawings.pdf

1211-chenp-2005-form 1.pdf

1211-chenp-2005-form 26.pdf

1211-chenp-2005-form 3.pdf

1211-chenp-2005-form 5.pdf

1211-chenp-2005-form18.pdf

1211-chenp-2005-pct.pdf

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Patent Number

241558

Indian Patent Application Number

1211/CHENP/2005

PG Journal Number

29/2010

Publication Date

16-Jul-2010

Grant Date

13-Jul-2010

Date of Filing

10-Jun-2005

Name of Patentee

QUALCOMM INCORPORATED

Applicant Address

5775 MOREHOUSE DRIVE, SAN DIEGO, ALIFORNIA 92121,

Inventors:

#	Inventor's Name	Inventor's Address
1	KETCHUM, JOHN, W	CANDLEBERRY LANE, HARVARD, MA 01451,
2	WALLACE, MARK, S	4 MADEL LANE, BEDFORD, MA 01730, USA
3	GAAL, PETER	4678 VEREDA MAR DEL SOL, SAN DIEGO, CALIFORNIA 92130, USA

PCT International Classification Number

H04L 25/02

PCT International Application Number

PCT/US03/39392

PCT International Filing date

2003-12-09

PCT Conventions:

#	PCT Application Number	Date of Convention	Priority Country
1	60/432,760	2002-12-11	U.S.A.
2	10/729,070	2003-12-04	U.S.A.