On the user selection for MIMO broadcast channels

Bayesteh, Alireza; Khandani, Amir K.

doi:10.1109/isit.2005.1523763

Cited by 79 publications

(109 citation statements)

References 25 publications

(41 reference statements)

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“…Through the use of a simple norm-threshold feedback protocol, the maximal scaling of the sum rate is achievable [25]. In [26], only those users with the singular values above a set threshold are allowed to feed back the channel information to the BS. In [27], a low-complexity suboptimal user selection scheme, which was referred to as the semi-orthogonal user selection (SUS) algorithm, was proposed.…”

Section: Introductionmentioning

confidence: 99%

“…In [27], a low-complexity suboptimal user selection scheme, which was referred to as the semi-orthogonal user selection (SUS) algorithm, was proposed. When the user selection algorithms in [26] and [27] are combined with zero-forcing (ZF) beamforming, the optimum sum-rate throughput can be asymptotically achieved when K goes to infinity.…”

Section: Introductionmentioning

confidence: 99%

“…We may regard model (1) as a general description of several scenarios in downlink transmissions as follows: [20], [26], [27], [30], [35]. In this paper, we focus on the problem of multiuser scheduling in the last scenario, where multiple users are scheduled simultaneously at any time.…”

Section: Introductionmentioning

confidence: 99%

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MIMO Broadcast Scheduling with Limited Feedback

Zhang

Letaief

2007

IEEE J. Select. Areas Commun.

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Abstract-We consider multiuser scheduling with limited feedback of partial channel state information in MIMO broadcast channels. By using spatial multiplexing at the base station (BS) and antenna selection for each user, we propose a multiuser scheduling method that allocates independent information streams from all M transmit antennas to the M most favorable users with the highest signal-to-interference-plus-noise ratio (SINR). A close approximation of the achievable sum-rate throughput for the proposed method is obtained and shown to match the simulation results very well. Moreover, two reduced feedback scheduling approaches are proposed. In the first approach, which we shall refer to as selected feedback scheduling, the users are selected based on their SINR compared to a predesigned threshold. Only those selected users are allowed to feed back limited information to the BS. The resultant feedback load and achievable throughput are derived. It will then be demonstrated that with a proper choice of the threshold, the feedback load can be greatly reduced with a negligible performance loss. The second reduced feedback scheduling approach employs quantization for each user, in which only few bits of quantized SINR are fed back to the BS. Performance analysis will show that even with only 1-bit quantization, the proposed quantized feedback scheduling approach can exploit the multiuser diversity at the expense of slight decrease of throughput.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MIMO Broadcast Scheduling with Limited Feedback

Zhang

Letaief

2007

IEEE J. Select. Areas Commun.

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“…This limitation, however, is acceptable in real systems, where spatial division multiplexing access (SDMA) is used in conjunction with single user MIMO transmissions using adaptive switching [21]. Therefore, SDMA targets high cell throughput, which is optimal with infinite number of users in the network [22], while spatial multiplexing to a single user targets high peak rates.…”

Section: System Modelmentioning

confidence: 99%

A low complexity linear multiuser MIMO beamforming system with limited feedback

Chae

Mazzarese

Jindal

et al. 2008

2008 42nd Annual Conference on Information Sciences and Systems

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Abstract-In this paper, we propose a low complexity linear multiuser beamforming system for the multiple-input multipleoutput (MIMO) broadcast channel. We consider the specific case of transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which an iterative algorithm is required to design the beamformers, we first provide a low complexity non-iterative solution via the generalized eigenvector decomposition to jointly optimize transmit beamforming and receive combining vectors. The proposed beamforming technique works for two or more transmit and receive antennas where perfect channel state information is available at the transmitter. To enable practical implementation, a new non-uniform limited feedback algorithm is also proposed that exploits the structure of the algorithm to avoid full channel quantization especially for two transmit antenna systems. The feedback overhead is independent of the number of receive antennas. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.

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“…In general, user selection problems are combinatorial problems, and the complexity required to solve the problems could be prohibitively high for a large multiuser MIMO system. Thus, low-complexity suboptimal selection strategies are considered in [14][15][16][17][18][19][20][21], at the expense of degraded performance. In [14][15][16][17], a single antenna is selected at a time to maximize the throughput based on greedy selection schemes.…”

Section: Introductionmentioning

confidence: 99%