Box-Relaxation for BPSK Recovery in Massive MIMO: A Precise Analysis under Correlated Channels

Alrashdi, Ayed M.; Sifaou, Houssem; Kammoun, Abla; Alouini, Mohamed‐Slim; Al-Naffouri, Tareq Y.

doi:10.1109/icc40277.2020.9149198

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…This work characterizes the asymptotic performance of a multiuser MIMO setting in which users employ the MA-SM for uplink transmission and the receiver uses an RLS-based detector to jointly detect the transmitted signals. There are some earlier works that investigate special cases of this setting; e.g., [54]- [57]. Nevertheless, the analyses and results of this paper are new in various respects:…”

Section: Contributions and Organizationmentioning

confidence: 89%

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

Bereyhi¹,

Asaad²,

Gäde³

et al. 2020

Preprint

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This paper characterizes the performance of massive multiuser spatial modulation MIMO systems, when a regularized form of the least-squares method is used for detection. For a generic distortion function and right unitarily invariant channel matrices, the per-antenna transmit rate and the asymptotic distortion achieved by this class of detectors is derived. Invoking an asymptotic characterization, we address two particular applications. Namely, we derive the error rate achieved by the computationallyintractable optimal Bayesian detector, and we propose an efficient approach to tune a LASSO-type detector.We further validate our derivations through various numerical experiments.

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Section: Contributions and Organizationmentioning

confidence: 89%

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

Bereyhi¹,

Asaad²,

Gäde³

et al. 2020

Preprint

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“…assumptions on the entries of the channel matrix [35][36][37][38][39][40][41]. For correlated channel matrices, the cGMT was recently used in [42,43] to characterise the performance of the Box-relaxation and the least absolute shrinkage and selection operator (LASSO) detectors, respectively. However, these references assume the ideal case of perfect knowledge of the CSI, which is impossible to obtain in practice [7], while this work deals with the more difficult and common scenario of imperfect CSI.…”

Section: Introductionmentioning

confidence: 99%

“…Previous works on the performance analysis using the cGMT involve either considering the analysis of massive MIMO systems with perfect CSI with uncorrelated channels, such as in [35,[44][45][46], imperfect CSI systems with uncorrelated channels [11,47], or recently perfect CSI systems but with correlated channels [42,43]. None of the previous papers considers both channel correlations and channel estimation errors.…”

Section: Introductionmentioning

confidence: 99%

Asymptotic characterisation of regularised zero‐forcing receiver for imperfect and correlated massive multiple‐input multiple‐output systems

Alrashdi

2022

IET Signal Processing

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In this work, the authors present an asymptotic high-dimensional analysis of the regularised zero-forcing receiver in terms of its mean-squared error (MSE) and bit error rate (BER) when used for the recovery of binary phase-shift keying (BPSK) modulated signals in a massive multiple-input multiple-output communication system. The asymptotic analysis is carried out when the system dimensions grow simultaneously large at a fixed ratio. The authors assume that the channel matrix is spatially correlated and not perfectly known. The authors use the linear minimum mean-squared error (LMMSE) method to estimate the channel matrix. The asymptotic approximations of the MSE and BER enable us to solve various practical optimisation problems. Under MSE/BER minimisation, the authors derive (1) the optimal regularisation factor for RZF; (2) the optimal power allocation scheme. The numerical simulations match the derived asymptotic expressions, even when the problem dimensions are small.

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Optimum GSSK Transmission in Massive MIMO Systems Using the Box-LASSO Decoder

Alrashdi

Alrashdi²,

Eleiwa³

2022

IEEE Access

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We propose in this work to employ the Box-LASSO, a variation of the popular LASSO method, as a low-complexity decoder in a massive multiple-input multiple-output (MIMO) wireless communication system. The Box-LASSO is mainly useful for detecting simultaneously structured signals such as signals that are known to be sparse and bounded. One modulation technique that generates essentially sparse and bounded constellation points is the so-called generalized space-shift keying (GSSK) modulation. In this direction, we derive high dimensional sharp characterizations of various performance measures of the Box-LASSO such as the mean square error, probability of support recovery, and the element error rate, under independent and identically distributed (i.i.d.) Gaussian channels that are not perfectly known. In particular, the analytical characterizations can be used to demonstrate performance improvements of the Box-LASSO as compared to the widely used standard LASSO. Then, we can use these measures to optimally tune the involved hyper-parameters of Box-LASSO such as the regularization parameter. In addition, we derive optimum power allocation and training duration schemes in a training-based massive MIMO system. Monte Carlo simulations are used to validate these premises and to show the sharpness of the derived analytical results.

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Box-Relaxation for BPSK Recovery in Massive MIMO: A Precise Analysis under Correlated Channels

Cited by 6 publications

References 23 publications

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

Detection of Spatially Modulated Signals via RLS: Theoretical Bounds and Applications

Asymptotic characterisation of regularised zero‐forcing receiver for imperfect and correlated massive multiple‐input multiple‐output systems

Optimum GSSK Transmission in Massive MIMO Systems Using the Box-LASSO Decoder

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