Linear Precoding for MIMO Multiple Access Channels with Finite Discrete Inputs

Wang, Mingxi; Zeng, Weiliang; Xiao, Chengshan

doi:10.1109/twc.2011.091911.110508

Cited by 58 publications

(79 citation statements)

References 29 publications

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“…Linear precoder design that maximizes the average mutual information of MIMO fading channels with finite-alphabet inputs was proposed in [10], in which the statistical channel state information (CSI) was assumed to be known at the transmitter side. In [11], the authors studied a linear precoding for MIMO channels with finite discrete inputs, in which the capacity region for the MU-MIMO has been derived. In [12] a linear precoder design for MU-MIMO transceivers under finite alphabet inputs was proposed, where the optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions were studied.…”

Section: Introductionmentioning

confidence: 99%

Sum Rate and Fairness Analysis for the MU-MIMO Downlink Under PSK Signalling: Interference Suppression vs Exploitation

Salem

Masouros

Wong

2019

IEEE Trans. Commun.

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In this paper, we analyze the sum rate performance of multi-user multiple-input multiple-output (MU-MIMO) systems, with a finite constellation phase-shift keying (PSK) input alphabet. We analytically calculate and compare the achievable sum rate in three downlink transmission scenarios: 1) without precoding, 2) with zero forcing (ZF) precoding 3) with closed form constructive interference (CI) precoding technique. In light of this, new analytical expressions for the average sum rate are derived in the three cases, and Monte Carlo simulations are provided throughout to validate the analysis. Furthermore, based on the derived expressions, a power allocation scheme that can ensure fairness among the users is also proposed. The results in this work demonstrate that, the CI strictly outperforms the other two schemes, and the performance gap between the considered schemes increases with increase in the MIMO size. In addition, the CI provides higher fairness and the power allocation algorithm proposed in this paper can achieve maximum fairness index.Index Terms-Finite constellation signaling, zero forcing, constructive interference, phase-shift keying signaling, multiple-input multiple-output.

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Section: Introductionmentioning

confidence: 99%

Sum Rate and Fairness Analysis for the MU-MIMO Downlink Under PSK Signalling: Interference Suppression vs Exploitation

Salem

Masouros

Wong

2019

IEEE Trans. Commun.

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“…In another study [49], the optimal power allocation policy that maximizes the mutual information, named as mercury/water-filling, was shown to be a generalization to the well-known water-filling algorithm. Multi-access systems were studied as well [47], where linear precoding matrices are obtained in order to maximize the weighted sum rate. An extension of the same analysis was performed in scenarios in which transmitters have only statistical information about the wireless channels [48].…”

Section: Introductionmentioning

confidence: 99%

Effective Capacity in MIMO Channels With Arbitrary Inputs

Hammouda

Akın

Gursoy

et al. 2018

IEEE Trans. Veh. Technol.

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Abstract-Recently, communication systems that are both spectrum and energy efficient have attracted significant attention. Different from the existing research, we investigate the throughput and energy efficiency of a general class of multipleinput and multiple-output systems with arbitrary inputs when they are subject to statistical quality-of-service (QoS) constraints, which are imposed as limits on the delay violation and buffer overflow probabilities. We employ the effective capacity as the performance metric, which is the maximum constant data arrival rate at a buffer that can be sustained by the channel service process under specified QoS constraints. We obtain the optimal input covariance matrix that maximizes the effective capacity under a short-term average power constraint. Following that, we perform an asymptotic analysis of the effective capacity in the low signal-to-noise ratio and large-scale antenna regimes. In the low signal-to-noise ratio regime analysis, in order to determine the minimum energy-per-bit and also the slope of the effective capacity versus energy-per-bit curve at the minimum energy-per-bit, we utilize the first and second derivatives of the effective capacity when the signal-to-noise ratio approaches zero. We observe that the minimum energy-per-bit is independent of the input distribution, whereas the slope depends on the input distribution. In the large-scale antenna analysis, we show that the effective capacity approaches the average transmission rate in the channel with the increasing number of transmit and/or receive antennas. Particularly, the gap between the effective capacity and the average transmission rate in the channel, which is caused by the QoS constraints, is minimized with the number of antennas. In addition, we put forward the non-asymptotic backlog and delay violation bounds by utilizing the effective capacity. Finally, we substantiate our analytical results through numerical illustrations.Index Terms-Effective capacity, energy efficiency, large-scale antenna regime, minimum energy-per-bit, multiple-antenna systems, mutual information, optimal input covariance, quality of service constraints.

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“…Although the aforementioned studies are all based on Gaussian input assumption, linear precoder optimization for finite alphabet inputs has been investigated for several scenarios including single user MIMO [11,12], MIMO BC [13], MIMO multiple access channels (MAC) [14], MIMO relay channels [15,16], MIMO hybrid-ARQ (HARQ) [17], as well as MIMO wiretap channels [18]. Each case demonstrates performance gain over the laissez faire use of their Gaussian based precoder design counterpart.…”

Section: Introductionmentioning

confidence: 99%

Cooperative multi-cell MIMO downlink precoding for finite-alphabet inputs

Wang

Ding

et al. 2014

2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This work studies the design of linear precoders for cooperative multi-cell MIMO downlink systems with finite alphabet inputs. Traditionally, multi-cell MIMO downlink precoder designs rely on Gaussian input assumption, which may lead to performance loss when the true inputs admit discrete non-Gaussian symbols. In this work, we present optimized precoders by maximizing weighted sum rate (of finite-alphabet-input) under a set of single basestation power constraints. Specifically, we propose a simple gradient algorithm for general multi-cell MIMO downlink channel and a block diagonalization gradient algorithm while supporting interference cancellation.

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Linear Precoding for MIMO Multiple Access Channels with Finite Discrete Inputs

Cited by 58 publications

References 29 publications

Sum Rate and Fairness Analysis for the MU-MIMO Downlink Under PSK Signalling: Interference Suppression vs Exploitation

Sum Rate and Fairness Analysis for the MU-MIMO Downlink Under PSK Signalling: Interference Suppression vs Exploitation

Effective Capacity in MIMO Channels With Arbitrary Inputs

Cooperative multi-cell MIMO downlink precoding for finite-alphabet inputs

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