WO2010005999A2

WO2010005999A2 - Multiple input multiple output (mimo) rank adaptation with uniform channel decomposition

Info

Publication number: WO2010005999A2
Application number: PCT/US2009/049853
Authority: WO
Inventors: Ron Porat; Yi Jiang
Original assignee: Wi-Lan, Inc.
Priority date: 2008-07-07
Filing date: 2009-07-07
Publication date: 2010-01-14
Also published as: CN102144360B; EP2304881A2; WO2010005999A3; CN102144360A; EP2304881A4

Abstract

The subject matter disclosed herein provides methods and apparatus for closed loop operation of a wireless system implementing multiple input multiple output (MIMO). In one aspect, there is provided a method. The method may include determining, at a client station, a plurality of singular vectors for channels used in a multiple-input multiple-output (MIMO) transmission from a base station to a client station. Moreover, the client station may provide a first indication to the base station to use a singular value decomposition, when one of the singular vectors is substantially larger than the other singular vectors. Furthermore, the base station may provide a second indication to the base station to use a uniform channel decomposition, when one of the singular vectors is not substantially larger than the other singular vectors. Related systems, apparatus, methods, and/or articles are also described.

Description

MULTIPLE INPUT MULTIPLE OUTPUT (MIMO) RANK ADAPTATION WITH UNIFORM CHANNEL DECOMPOSITION

CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority of U.S. Provisional Patent Application Serial No. 61/078,767, filed on July 7, 2008, entitled "MULTIPLE INPUT MULTIPLE OUTPUT (MIMO) RANK ADAPTATION WITH UNIFORM CHANNEL DECOMPOSITION," the disclosure of which is hereby incorporated by reference in its entirety.

FIELD [0002] The subject matter described herein relates to wireless communications.

BACKGROUND

[0003] Multiple-input and multiple-output (MIMO) is typically used in wireless communications to enhance performance, when compared to non-MIMO approaches. For example, multiple antennas may be implemented at the transmitter and/or the receiver to improve performance by providing, in some implementations, enhanced throughput and range. Often, these performance enhancements may be obtained without substantial increases in transmitted power and/or bandwidth, hence the appeal of MIMO. However, MIMO typically comes at the cost of complex processing at the transmitter and at the receiver.

SUMMARY [0004] The subject matter disclosed herein provides methods and apparatus for closed loop operation of a wireless system implementing multiple input multiple output (MIMO). [0005] In one aspect, there is provided a method. The method may include determining, at a client station, a plurality of singular vectors for channels used in a multiple- input multiple-output (MIMO) transmission from a base station to a client station. Moreover, the client station may provide a first indication to the base station to use a singular value decomposition, when one of the singular vectors is substantially larger (e.g., stronger) than the other singular vector. Furthermore, the client station may provide a second indication to the base station to use a uniform channel decomposition, when one of the singular vectors is not substantially larger than the other singular vector.

[0006] In another aspect, there is provided a method. The method may include receiving, at a base station from a client station, a first indication to use a singular value decomposition, when one of the singular vectors is substantially larger than the other singular vector. Moreover, the base station may receive from the client station, a second indication to use a uniform channel decomposition, when one of the singular vectors is not substantially larger than the other singular vector. Furthermore, the base station may be configured for transmission based on at least one of the first indication and the second indication received from the client station.

[0007] The details of one or more variations of the subject matter described herein are set forth in the accompanying drawings and the description below. Features and advantages of the subject matter described herein will be apparent from the description and drawings, and from the claims.

DESCRIPTION OF DRAWINGS [0008] In the drawings, [0009] FIG. 1 depicts a block diagram of a network including client stations and base stations;

[0010] FIG. 2A depicts a block diagram of a client station including a channel estimator for determining whether to use a singular value decomposition or a uniform channel decomposition based on the strength of singular vectors;

[0011] FIG. 2B depicts a process for determining whether to use a singular value decomposition or a uniform channel decomposition based on the strength of singular vectors;

[0012] FIG. 3A depicts a block diagram of a base station;

[0013] FIG. 3B depicts a process, at the base station, configured to use a singular value decomposition or a uniform channel decomposition; and

[0014] FIG. 4 depicts simulation results.

[0015] Like labels are used to refer to same or similar items in the drawings.

DETAILED DESCRIPTION [0016] FIG. 1 is a simplified functional block diagram of an embodiment of a wireless communication system 100. The wireless communication system 100 includes a plurality of base stations HOA and HOB, each supporting a corresponding service or coverage area 112A and 112B. The base stations are capable of communicating with wireless devices within their coverage areas. For example, the first base station 11OA is capable of wirelessly communicating with a first client station 114A and a second client station 114B within the coverage area 112A. The first client station 114A is also within the coverage area 112B and is capable of communicating with the second base station HOB. In this description, the communication path from the base station to the client station is referred to as a downlink 116A and the communication path from the client station to the base station is referred to as an uplink 116B.

[0017] Although for simplicity only two base stations are shown in FIG. 1, a typical wireless communication system 100 includes a much larger number of base stations. The base stations 11OA and HOB can be configured as cellular base station transceiver subsystems, gateways, access points, radio frequency (RF) repeaters, frame repeaters, nodes, or any wireless network entry point.

[0018] The base stations HOA and 11OB can be configured to support an omnidirectional coverage area or a sectored coverage area. For example, the second base station 11OB is depicted as supporting the sectored coverage area 112B. The coverage area 112B is depicted as having three sectors, 118A, 118B, and 118C. In typical embodiments, the second base station 11 OB treats each sector 118 as effectively a distinct coverage area.

[0019] Although only two client stations 114A and 114B are shown in the wireless communication system 100, typical systems are configured to support a large number of client stations. The client stations 114A and 114B can be mobile, nomadic, or stationary units. The client stations 114A and 114B are often referred to as, for example, mobile stations, mobile units, subscriber stations, wireless terminals, or the like. A client station can be, for example, a wireless handheld device, a vehicle mounted device, a portable device, client premise equipment, a fixed location device, a wireless plug-in accessory or the like. In some cases, a client station can take the form of a handheld computer, notebook computer, wireless telephone, personal digital assistant, wireless email device, personal media player, meter reading equipment or the like and may include a display mechanism, microphone, speaker and memory. [0020] In a typical system, the base stations HOA and HOB also communicate with each other and a network control module 124 over backhaul links 122A and 122B. The backhaul links 122A and 122B may include wired and wireless communication links. The network control module 124 provides network administration and coordination as well as other overhead, coupling, and supervisory functions for the wireless communication system 100.

[0021] In some embodiments, the wireless communication system 100 can be configured to support both bidirectional communication and unidirectional communication. In a bidirectional network, the client station is capable of both receiving information from and providing information to the wireless communications network. Applications operating over the bidirectional communications channel include traditional voice and data applications. In a unidirectional network, the client station is capable of receiving information from the wireless communications network but may have limited or no ability to provide information to the network. Applications operating over the unidirectional communications channel include broadcast and multicast applications. In one embodiment, the wireless system 100 supports both bidirectional and unidirectional communications. In such an embodiment, the network control module 124 is also coupled to external entities via, for example, content link 126 (e.g., a source of digital video and/or multimedia) and two-way traffic link 128.

[0022] The wireless communication system 100 can be configured to use Orthogonal Frequency Division Multiple Access (OFDMA) communication techniques. For example, the wireless communication system 100 can be configured to substantially comply with a standard system specification, such as IEEE 802.16 and its progeny or some other wireless standard such as, for example, WiBro, WiFi, Long Term Evolution (LTE), or it may be a proprietary system. The subject matter described herein is not limited to application to OFDMA systems or to the noted standards and specifications. The description in the context of an OFDMA system is offered for the purposes of providing a particular example only.

[0023] As used herein, IEEE 802.16 refers to one or more Institute of Electrical and Electronic Engineers (IEEE) Standard for Local and metropolitan area networks, Part 16: Air Interface for Fixed Broadband Wireless Access Systems, 1 October 2004, IEEE Standard for Local and metropolitan area networks, Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems, 26 February 2006, and any subsequent additions or revisions to the IEEE 802.16 series of standards.

[0024] In some embodiments, downlinks 116A-B and uplink 116C each represent a radio frequency (RF) signal. The RF signal may include data, such as voice, video, images, Internet Protocol (IP) packets, control information, and any other type of information. When IEEE-802.16 is used, the RF signal may use OFDMA. OFDMA is a multi-user version of orthogonal frequency division multiplexing (OFDM). In OFDMA, multiple access is achieved by assigning to individual users groups of subcarriers (also referred to as subchannels or tones). The subcarriers are modulated using BPSK (binary phase shift keying), QPSK (quadrature phase shift keying), QAM (quadrature amplitude modulation), and carry symbols (also referred to as OFDMA symbols) including data coded using a forward error-correction code.

[0025] In some embodiments, a base station is implemented using multiple-input and multiple-output (MIMO). When MIMO is used, a base station may include a plurality of antennas. For example, base station HOA may be configured for MIMO and include a precoder (described further below) coupled to two antennas for the MIMO transmission via downlinks 116A-B. The precoder is configured to perform "precoding," which refers to beamforming to support MIMO transmission at each of the antennas (e.g., using singular vectors to weight orthogonal "eigen-beams" transmitted via each of the antennas). A client station may include a plurality of antennas to receive the MIMO transmission sent via downlinks 116A-B. The client station may also combine the received signals, which may result in fewer errors and/or enhanced data transfer. Although the examples given herein are made in the context of MIMO, other smart antenna techniques may be used as well including MISO (multiple input, single output) and SIMO (single input, multiple output).

[0026] Moreover, when MIMO is used, the base station may perform precoding (which may use channel estimation information) to code, for each antenna, one or more streams of symbols for transmission over the corresponding antenna. In a closed loop feedback-based approach, the channel estimation information is provided by the client station to the base station. For example, a client station may receive each of the downlinks 116A-B transmitted by the antennas of the base station, decode the received downlink signals, determine channel estimation information for the decoded channels (e.g., subcarriers) in each of the received downlink signals, and then provide to the base station the determined channel estimation information. The channel estimation information provided by the client station may include singular vectors determined by the client station using a singular value decomposition (SVD). Although the channel estimation information is described as including singular vectors, the channel estimation information may also include other channel information, such as the signal-to-noise ratio of a subcarrier, carrier- to-noise ratio, a channel covariance matrix, a channel matrix, and the like.

[0027] The singular vectors may be determined for each of the channels (e.g., subcarriers) used by the antennas transmitting from the base station to the client station. For example, the base station may include two antennas, each of which transmits over a channel comprising one or more subcarriers. The client station may then determine singular vectors for the subcarriers. Moreover, the subcarriers (as well as other channel information) may be used to determine the so-called "strength" of the channel. For example, in the two antenna case, if the singular vector V₁ is substantially larger (e.g., when the eigen-subchannel of Vi is much larger than eigen-subchannel of V₂) than the singular vector V₂, then the client station may provide an indication to the base station to use a singular value decomposition at the precoder. However, if the singular vector V₁ is not substantially larger than the singular vector v₂, then the client station may provide an indication to the base station to use uniform channel decomposition when precoding at the base station. In some implementations, configuring the base station for a single stream of symbols using a singular value decomposition or a plurality of streams of symbols using a uniform channel decomposition (e.g., based on feedback from the client station indicating the "strength" of the singular vectors) provides enhanced performance, when compared to operation using only a singular value decomposition or only a uniform channel decomposition.

[0028] FIG. 2A depicts an exemplary client station, such as client station 114B. The client station 114B includes a plurality of antennas 220A -B for receiving the downlinks 116A-B, each transmitted by a base station, such as base station HOA, which implements MIMO as described further below. Although the examples described herein refer to two antennas at the base station and two antennas at the client station, other quantities of antennas can be used at the base station and the client station. The client station 114B also includes a radio interface 240, which may include other components, such as filters, converters (e.g., digital-to-analog converters and the like), symbol demappers, an Inverse Fast Fourier Transform (IFFT) module, and the like, to process the received MIMO transmission sent via downlinks 116A-B, to determine channel estimation information, and to decode any data, such as the symbols, carried by the downlinks. In some implementations, the client station 114B is also compatible with IEEE 802.16 and MIMO transmissions (which are sent via downlinks 116A-B), although MIMO implementations using other wireless technologies, such as LTE, WiBro, and the like, may also be implemented using the subject matter described herein. The client station 114B further includes a channel estimator 260 (described further below), a processor 220 for controlling client station 114B and for accessing and executing program code stored in memory 225.

[0029] For each of the MIMO transmissions sent via downlinks 116A-B and received at each of antennas 220A-B, the channel estimator 260 may determine channel estimation information, such as singular vectors determined using a singular value decomposition. Moreover, the channel estimator 260 may then provide, based on the strength of the determined singular vectors, an indication to the precoder at the base station to use a singular value decomposition or to use a uniform channel decomposition. When a singular value decomposition is used, the precoder at the base station provides, based on a singular value decomposition, a single stream of symbols for MIMO transmission. When uniform channel decomposition is used, the precoder at the base station provides, based on a uniform channel decomposition, a plurality of streams of symbols for MIMO transmission. The determined singular vectors v_/ and v_∑ (as well as other channel estimation information) may also be provided as feedback to the base station (e.g., as a management message transmitted via uplink 116C), which may be used at the precoder to configure for a singular value decomposition or a uniform channel decomposition.

[0030] In some embodiments, the singular vector V₁ based on the following:

V₁ = pζ cos6> + h₂ ^* sin θ e^jφ , and determines the singular vector v^ based on the following:

V₂ = h^ sm^' θ -h₂ ^* cosθ e^jφ . [0031] The determined singular vectors vj and v^ are then provided as feedback to the base station (e.g., as a management message transmitted via uplink 116C).

[0032] The following provides example implementations for determining the singular vectors v; and v_∑, although other approaches may be used as well. Generally, analog feedback may provide advantages, such as low complexity at the client station (e.g., a mobile station) and unbounded feedback accuracy which may be particularly important for multi-user MIMO with uncorrelated antennas or multi-base station MIMO.

[0033] In any case, there are several options for feedback, such as using a full channel matrix H, a covariance matrix H^H H , a strongest right singular vector, and two strongest right singular vectors. While feeding back the full channel matrix or channel covariance provides sufficient information for single user and/or multi-user MIMO, feeding back just the first or two strongest eigenvectors may reduce feedback overhead.

[0034] For example, a 4 antenna base station requires 8 complex values for feedback consisting of a full channel matrix or a covariance matrix, and only 3 complex values for the strongest right singular vector option and 5 complex values for two strongest right singular vectors option. An 8 antenna base station or two 4 antenna base stations in multi-base station MIMO will require 16, 7, and 13 values for the full channel matrix option, the strongest right singular vector option, and the two strongest right singular vectors option respectively. In addition, a typical operation of closed loop MIMO in frequency division duplex may require the client station (e.g., a mobile station) to send an initial estimate of the post precoding carrier-to- interference-plus-noise ratio (CINR). [0035] To further illustrate, the following provides an example of how the singular values of the channel is calculated as these will determine the post precoding SNR (signal to noise ratio).

[0036] Assume the channel matrix H is of dimension 2 by N (2xN), where N is greater than or equal to 2 (N>=2). The singular values can be found from the eigenvalues of

HH which is of a 2x2 dimension matrix, which can be solved based on the following two σ,² +σ₂ ² = Tr(HH") equations: ^σ^ ^{σi ~~ det}v^" ⁾ ₅ wherein Tr is trace and σi and σ₂ are the two singular values of H.

[0037] To calculate the singular value decomposition, the focus is on the matrix

H which is of size Nx2, and then find the correct singular vectors that are of size 2 and use

those singular vectors to find the left singular vectors which are the right singular vectors of H. [0038] First, the form of H^H is of the following form: H^H = V∑U^H , where U can be generally expressed as follows:

[0039] By the definition of the singular value decomposition, the first column of matrix U is calculated according to the following equation:

where ||x|| represents a Euclidean norm. The maximum Euclidean norm is the maximal singular value σ, . [0040] Denoting by ' the i^th row of matrix H and developing the above expression, the following equation is formed:

which can be expressed (e.g., via substitution) as follows:

<9 = argmax | \ |² COS² O¹H- I Zz₂ |² sin² θ + 2 \ It₁H₁ | sin θcosθ . θ

[0041] Differentiating and equating to zero, the following equation is formed:

tan 2Θ = ' ' ,

\ M² -\ h₂ r after which a CORDIC rotation is used to calculate cos θ and sin θ . Using UH^H = V∑, the strongest singular vector is determined by normalizing the following equation: V₁ = K₁ cos θ + h₂ ^* sin θ e^JΦ .

[0042] At this point, the strongest right singular vector has been finalized. To finalize the two right strongest singular vectors, a solution to the following equation is found: v₂ = Zz₁ ^* sin θ -h₂ ^* cos θ e^iΦ , which may be normalized.

[0043] To illustrate further, the singular value decomposition (SVD) is generally done through iterative procedures. But for the special case of N x 2 matrix, the decomposition may be done via a closed-form solution for singular value decomposition. For example, given a matrix of the following form:

, and then calculating the singular value decomposition as follows: H - UΣV^* where

*^'2 is a orthonormal matrix, and ∑ is of the following form:

Σ = CT₁ 0

U σ<> and V is of the following form: cos θ sin θ

V

^■ COS θc*^ώ

[0044] By the definition of the singular value decomposition, the first column of V, which is parameterized by θ and φ , is calculated according to the following equation:

where |-| represents for Euclidean norm, and this maximum Euclidean norm is the maximal

singular value CTj . Denoting a_h b, as the /th element of hi and h2, respectively, the solution of the θ and φ equation above can be derived based on the following equation:

N θ. ό — arg max y^~* Ia, cos θ -f h_t sin θe^'ϊ²

Θ4> ι=l

, si ^■n , 2² ^ A + o 2τR>*^» b%hιQπκ θt>-rt

[0045] Thus, the optimal φ = Z(∑f =_λa_lb^*) , which can be expressed as follows:

[0046] Further simplification of the θ and φ equation yields the following:

θ sin.0 cos 0,

and further constraining ¹^ I " -/ causes no loss of optimality, and equating the derivative to zero yields the following:

[0047] Hence, the following:

[0048] The value of θ may be calculated based on the above equation, which has only one solution in the interval 0 to π/2. However, since the solution of interest is cos θ and sin θ rather than θ itself, the following may be used to find a solution:

2 tan a t an 2Θ =

[0049] When α is denoted as follows:

[0050] The following equation results: l - tan²£

= α, 2 tan θ

which leads to the unique solution in θ over the interval of 0 to π/2 (i.e., # € f [θ0.. τ/2)) as follows:

tan<9 = V^'l + α² - a. and

and sin θ = cos $ * tan θ. [0051] Given the above cos θ and sin θ, V may be formed as follows:

V = ύn θe^JO - vι>Α θc'J^φ

and given the following:

UΣ = HV

[0052] and, given the following:

which is the first column of UΣ, the largest singular value has the following form.

wherein |-|| represents the Euclidean norm and the second equality follows from the θ symbol above. As such, the corresponding singular vector is U

U₁ =

[0053] Given the following equation,

θe^3Ψ _^ which is the second column of UΣ, and given

where "^F represents the

Frobenius norm, the smaller singular value is obtained as follows: σ-₂ - J∑H^a + ∑|6.

[0054] Thus, the corresponding singular vector is

V 11-. = .

[0055] Table 1 depicts example Matlab pseudo-code for determining the singular vectors as described above.

Table 1 Sample Pseudo-Code

[0056] FIG. 2B depicts a process 200 for determining, at a client station, whether to use a singular value decomposition or a uniform channel decomposition. The description of process 200 will also refer to FIG. 2A.

[0057] At 292, channel estimator 260 determines one or more singular vectors for the channels (e.g., subcarriers). For example, if base station 11OB transmits using MIMO over two antennas, channel estimator 260 may use a singular value decomposition to determine the singular vectors (e.g., singular vectors V₁ and v₂ also referred to as U] and u₂) for each of the channels.

[0058] At 294, channel estimator 260 determines whether one of the singular vectors is substantially larger (e.g., has a higher eigenvalue and thus is capable of more capacity) than the other singular vectors. For example, channel estimator 260 may normalize the determined singular vectors to have a range of zero to one. Given normalized vectors, a substantially larger singular vector V₁ may have a value close to or equal to one, while another singular vector may have a value close to zero. Continuing with this example, at 296, the channel estimator 260 provides an indication to base station 11 OA to use a singular value decomposition at the precoder. When a singular value decomposition is used at the base station, the precoder at base station HOB provides, based on singular value decomposition, a single stream of symbols for MIMO transmission. On the other hand, when the singular vector V₁ is not substantially larger than the other singular vector (e.g., singular vector vl does not have a value close to or equal to one), at 298, the channel estimator 260 provides an indication to the base station to use a uniform channel decomposition at the precoder. In some implementations, process 200 is stored as program code stored at memory 225.

[0059] FIG. 3 A depicts a base station, such as base station HOA. The base station 11 OA includes antennas 320A-B configured to transmit via downlinks 116A-B and configured to receive uplink 116C via at least one of antennas 320A-B. The base station 11OA further includes a radio interface 340 coupled to the antennas 320A-B, a precoder 360 (described further below), a processor 330 for controlling base station 11OA and for accessing and executing program code stored in memory 335. The radio interface 340 further includes other components, such as filters, converters (e.g., digital-to-analog converters and the like), mappers, a Fast Fourier Transform (FFT) module, and the like, to generate a MIMO transmission via downlinks 116A-B to receive the channel estimation information provided via uplink 116C, and to receive from a client station an indication of whether to use a singular value decomposition or a uniform channel decomposition at the base station when transmitting using MIMO to the client station. The indication thus serves as a form of rank adaptation. In some implementations, the base station HOA is also compatible with IEEE 802.16, and the RF signals of the MIMO downlinks 116A-B and uplink 116C are configured in accordance with OFDMA.

[0060] The radio interface 340 decodes the uplink 116C carrying an indication (e.g., a management message received from a client station) representative of whether the precoder is configured to perform a singular value decomposition or a uniform channel decomposition. The radio interface 340 may also decode uplink 116C carrying any channel estimation information (e.g., singular vectors determined at the client station), which are provided to the precoder 360. The precoder 360 receives the indication and configures for a singular value decomposition or a uniform channel decomposition. When a singular value decomposition is used, the precoder at the base station provides a single stream for MIMO transmission via the antennas 320A-B. When uniform channel decomposition is used, the precoder 360 uses any singular vectors vi and v₂ determined at the client station (as well as any other channel estimation information provided as feedback by the client station to the base station) to provide, based on a uniform channel decomposition, a plurality of symbols streams (e.g., two symbol streams) for MIMO transmission via each antennas 320A-B.

[0061] FIG. 3B depicts a process 300 to configure a base station to use a singular value decomposition adaptation or a uniform channel decomposition based on the strength singular vectors determined by a client station. The description of process 300 will refer to FIGs. 2A and 3 A as well.

[0062] At 393, a base station, such as base station HOA, receives from a client station an indication representative of whether to use a singular value decomposition or a uniform channel decomposition, when transmitting to the client station. For example, client station 114B may provide the indication as a management message via uplink 116C. At 394, the received indication is provider to a precoder, such as precoder 360. When the indication corresponds to a singular value decomposition, the precoder 360 provides, based on a singular value decomposition, a single stream of symbols for MIMO transmission via antennas 320A-B. When the indication corresponds to uniform channel decomposition, the precoder 360 uses any singular vectors vi and V₂ determined at the client station (as well as any other channel estimation information provided as feedback by the client station to the base station) to provide, based on a uniform channel decomposition, a plurality of symbols streams (e.g., two symbol streams) for MIMO transmission via antennas 320A-B. At 396, the base station transmits to the client station based on the indication provided by the client station. For example, the base station HOA configures transmission to the client station 114B based whether the client station 114B has indicated the base station HOA should transmit in accordance with a singular value decomposition or a uniform channel decomposition. In the case of a uniform channel decomposition, the client station 114B may also provide channel estimation information, such as singular vectors, a channel covariance matrix, and the like.

[0063] Moreover, typically, rank-2 operation decomposes the channel using singular value decomposition and transmits on the two eigen-subchannels. This orthogonalization of the channel may cause reduced performance in some implementations, which do not use matched modulation and coding schemes to each singular vector. To address that issue, the eigen- subchannels may be rotated mathematically such that the two subchannels become two layers with identical output SINR when decoded using successive interference cancellation (SIC) receiver. This is the underlying essence of uniform channel decomposition. [0064] To further illustrate, rank adaptation may be performed using uniform channel decomposition in a practical closed loop MIMO operation. The client station (e.g., mobile station) or base station decides on the best rank for the transmission according to the channel, SINR and other considerations using for example a capacity criterion. In case of rank- 1 transmission, the strongest singular vector is used. In case of rank-2 transmission, the precoder is calculated based on the uniform channel decomposition. In order to calculate the uniform channel decomposition, information about the channel's right singular vectors and singular values are required. This is best facilitated in frequency division duplex by the client station feeding back analog feedback of the channel or channel covariance matrix. Alternatively, the two singular vectors and the ratio of the singular values may also be sent as feedback. Moreover, the feedback may be facilitated by using codebooks and feeding back for rank-2 one extra value representing the ratio of the singular values.

[0065] FIG. 4 depicts plots of simulation results showing a comparison of rank-2 transmission between the uniform channel decomposition method and a regular singular value decomposition in 2x2 and 2x4 configurations using a GSM TU channel, wherein one precoder per 9 subcarriers (bin) is used and 6 bits per second per hertz is the combined two stream spectral efficiency. FIG. 4 depicts the gain of uniform channel decomposition approach.

[0066] In some implementations, process 300 is stored as program code stored at memory 335. The subject matter described herein may be embodied in systems, apparatus, methods, and/or articles depending on the desired configuration. Base station HOA (or one or more components therein) can be implemented using one or more of the following: a processor executing program code, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), an embedded processor, a field programmable gate array (FPGA), and/or combinations thereof. Client station 114B (or one or more components therein) can be implemented using one or more of the following: a processor executing program code, an application-specific integrated circuit (ASIC), a digital signal processor (DSP), an embedded processor, a field programmable gate array (FPGA), and/or combinations thereof. These various implementations may include implementation in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device. These computer programs (also known as programs, software, software applications, applications, components, program code, or code) include machine instructions for a programmable processor, and may be implemented in a high-level procedural and/or object- oriented programming language, and/or in assembly/machine language. As used herein, the term "machine-readable medium" refers to any computer program product, computer-readable medium, apparatus and/or device (e.g., magnetic discs, optical disks, memory, Programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. Similarly, systems are also described herein that may include a processor and a memory coupled to the processor. The memory may include one or more programs that cause the processor to perform one or more of the operations described herein.

[0067] Although a few variations have been described in detail above, other modifications or additions are possible. In particular, further features and/or variations may be provided in addition to those set forth herein. For example, the above closed form singular value decomposition may be used in other applications, such as at the base station to determine singular vectors for a sounder transmitted by the client station to the base station. Moreover, the implementations described above may be directed to various combinations and subcombinations of the disclosed features and/or combinations and subcombinations of several further features disclosed above. In addition, the logic flow depicted in the accompanying figures and/or described herein does not require the particular order shown, or sequential order, to achieve desirable results. Other embodiments may be within the scope of the following claims.

Claims

WHAT IS CLAIMED:

1. A method comprising: determining, at a client station, a plurality of singular vectors for channels used in a multiple-input multiple-output (MIMO) transmission from a base station to the client station; providing, by the client station, a first indication to the base station to use a singular value decomposition, when one of the singular vectors is substantially larger than the other singular vector; and providing, by the client station, a second indication to the base station to use a uniform channel decomposition, when one of the singular vectors is not substantially larger than the other singular vector.

2. The method of claim 1, further comprising: receiving a single stream of symbols, when one of the singular vectors is substantially larger than the other singular vector.

3. The method of claim 1, further comprising: receiving a plurality of streams of symbols, when one of the singular vectors is not substantially larger than the other singular vector.

4. A method comprising: receiving, at a base station from a client station, a first indication to use a singular value decomposition, when one of the singular vectors is substantially larger than the other singular vector; receiving, at the base station from the client station, a second indication to use a uniform channel decomposition, when one of the singular vectors is not substantially larger than the other singular vector; and configuring the base station for transmission based on at least one of the first indication and the second indication received from the client station.