Sum-Rate Analysis of Multiuser MIMO System with Zero-Forcing Transmit Beamforming

Peng, Lü; Yang, Hong-Chuan

doi:10.1109/icc.2008.774

Cited by 10 publications

(22 citation statements)

References 14 publications

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“…Users in the same group are affected by the correlation between them. We assume that the BS knows the downlink channel matrix of all users through channel reciprocity in TDD systems .…”

Section: System Modelmentioning

confidence: 99%

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SINR loss and user selection in massive MU‐MISO systems with ZFBF

Chang

Zeng

et al. 2019

ETRI Journal

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Separating highly correlated users can reduce the loss caused by spatial correlation (SC) in multiuser multiple‐input multiple‐output (MU‐MIMO) systems. However, few accurate analyses of the loss caused by SC have been conducted. In this study, we define signal‐to‐interference‐plus‐noise ratio (SINR) loss to characterize it in multiuser multiple‐input single‐output (MU‐MISO) systems, and use coefficient of correlation (CoC) to describe the SC between users. A formula is deduced to show the accurate relation between SINR loss and CoC. Based on this relation, we propose a user selection method that utilizes CoC to minimize the average SINR loss of users in massive MU‐MISO systems. Simulation results verify the correctness of the relation and show that the proposed user selection method is very effective at reducing the loss caused by SC in massive MU‐MISO systems.

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“…Users in the same group are affected by the correlation between them. We assume that the BS knows the downlink channel matrix of all users through channel reciprocity in TDD systems .…”

Section: System Modelmentioning

confidence: 99%

“…There are primarily two types of user selection methods that use CoC as the SC metric to reduce the loss. In , thresholds of CoC are set to obtain semi‐orthogonal users for user selection. To avoid performance loss, only semi‐orthogonal users can be served in the same group.…”

Section: Introductionmentioning

confidence: 99%

SINR loss and user selection in massive MU‐MISO systems with ZFBF

Chang

Zeng

et al. 2019

ETRI Journal

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show abstract

“…Let be corresponding parameters in (8) and (9) for which the inequalities become tight. Then, for any λ ∈ [0, 1], we have that λC (1) c + (1 − λ)C (2) c ≤ log det I n + H c,c Σ n c (1) ,c (1) H + c,c (20) where C * de f = λC (1) c + (1 − λ)C (2) c and the last inequality follows from Jensen's inequality. Similarly…”

Section: Sum-rate Capacity Analysismentioning

confidence: 99%

“…To reduce its complexity, we use zero-forcing beamforming (ZFBF) technique. The basic idea of ZFBF is that each user is assigned one column vector of the pseudoinverse of the downlink channel matrix [20]. Moreover, ZFBF can effectively eliminate interuser interference and improve channel capacity.…”

Section: Sum-rate Capacity Analysismentioning

confidence: 99%

Cognitive Radio MIMO Gaussian Broadcast Channels with the Power Constraint

Xiao

Ouyang

Wang

2011

Wireless Pers Commun

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The cognitive radio multiple-input multiple-output Gaussian broadcast channels are studied where multiple antennas are available for both primary users and secondary users in a spectrum sharing environment, and the sum-rate capacity is also obtained under both the SUs' transmit power constraint and interference power constraint at the primary receivers. The paper principally consists of two steps. First, a duality technique and dirty paper coding are adopted to simplify the channels. Second, we propose an iterative power allocation algorithm to obtain the maximum sum-rate capacity and examine the effects of the constraint parameters on the concerned quantities. Finally, numerical simulation results are presented to validate the proposed theoretical analysis.

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“…For scalar and vector feedback cases, the authors obtain analytical expressions for the sum rate. A sum rate analysis on a semiorthogonal user selection algorithm combined with ZFBF at the BS is provided in [4] for two transmit antennas. Difficulty with ZFBF performance analysis is that the subchannel gains are the inverse of the diagonal elements of a matrix which has an inverted complex Wishart distribution.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Optimum Zero-Forcing Beamforming with Greedy User Selection

Özyurt

Torlak

2012

IEEE Commun. Lett.

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Abstract-In this letter, an exact performance analysis is presented on the sum rate of zero-forcing beamforming with a greedy user scheduling algorithm in a downlink system. Adopting water-filling power allocation, we derive a compact form for the joint probability density function of the scheduled users' squared subchannel gains when a transmitter with multiple antennas sends information to at most two scheduled users with each having a single antenna. The analysis is verified by numerical results.Index Terms-Zero-forcing beamforming, water-filling power allocation, user scheduling, sum rate.

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Sum-Rate Analysis of Multiuser MIMO System with Zero-Forcing Transmit Beamforming

Cited by 10 publications

References 14 publications

SINR loss and user selection in massive MU‐MISO systems with ZFBF

SINR loss and user selection in massive MU‐MISO systems with ZFBF

Cognitive Radio MIMO Gaussian Broadcast Channels with the Power Constraint

Performance Analysis of Optimum Zero-Forcing Beamforming with Greedy User Selection

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