Non-Coherent Massive MIMO Systems: A Constellation Design Approach

Xie, Huiqiang; Xu, Weiyang; Ngo, Hien Quoc; Li, Bing

doi:10.1109/twc.2020.2978806

Cited by 30 publications

(21 citation statements)

References 25 publications

(41 reference statements)

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“…Concerning non-coherent detection in massive MIMO systems, previous work have been conducted utilizing energy-based schemes. For example, in [12] they propose an energydetection-based joint constellation design approaches to detect multi-user transmitted signals,…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

Mahmoud¹,

El-Mahdy²,

Fischer³

2021

Preprint

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<div>In this work, non-coherent massive MIMO differential phase-shift keying modulation (DPSK) detection is considered to get rid of the complexity of channel estimation. However, most of the well-performing DPSK detection techniques require high computational complexity at the receiver. The use of deep-learning is proposed for detecting the transmitted DPSK symbols over a single-user massive MIMO system. We provide a multiple-symbol differential detection implementation using deep-learning. Two deep-learning-based multiple-symbol differential detection receiver designs are proposed and compared with differential detection (DD), decision-feedback differential detection (DFDD), and multiple-symbol differential detection (MSDD) for the same system parameters. Where multiple-symbol differential sphere detection (MSDSD) is used to implement MSDD. The results show that the proposed deep-learning-based classification neural networks outperform decision-feedback differential detection and achieve an optimal performance compared to conventional multiple-symbol differential detection implemented by multiple-symbol differential sphere detection.</div>

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Section: Introductionmentioning

confidence: 99%

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

Mahmoud¹,

El-Mahdy²,

Fischer³

2021

Preprint

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“…Noncoherent MIMO is insensitive to time-varying channels, and the receiving equipment is simple, which has become one of the main research fields of MIMO in recent years. The main research directions of noncoherent MIMO focus on noncoherent Grassmannian MIMO [1][2][3][4][5], differential detection MIMO [6][7][8][9][10][11], and energy detection MIMO [12][13][14][15][16][17][18][19][20][21][22][23][24][25]. Among them, Grassmannian MIMO demands no or only partial CSI for demodulation, which applies to high SNR and high-speed mobile scenarios [1].…”

Section: Introductionmentioning

confidence: 99%

“…The distribution curve by Gaussian approximation has a good match with the curve of the original received signal. Furthermore, the system capacity boundary of Gaussian approximation [24] and the theoretical BER formula in the closed form [25] are deduced.…”

mentioning

confidence: 99%

Performance Analysis on Spatial MFSK Modulation with Energy Detection

Qiu

Zheng

et al. 2021

Preprint

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Noncoherent multiple-input multiple-output （MIMO）detection in fast fading environments has received attention in recent years since less influence by factors such as phase fluctuations and the low requirements for channel estimation and synchronization. Spatial MFSK modulation with energy detection is different from conventional noncoherent MIMO in that it can obtain higher spatial multiplexing, but with the introduction of the nonlinear square-law operation, the analysis of its detection performance needs to be solved. This paper analyzes the theoretical symbol error rate (SER) performance of the Spatial MFSK modulation with energy detection. The noise of the MIMO system by energy detection conform to the generalized gamma distribution. Based on this distribution, the optimal decision rule of the system and the theoretical SER formula are derived. Numerical results show that the theoretical SER formula fits well with the simulation results of the system under the condition of high signal-to-noise ratio (SNR).

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“…Until recently, energy detection (ED)-based non-coherent massive single-input multipleoutput (SIMO) systems prove to be promising and draw much attention [6]. By using large antenna arrays, ED-based receiver could operate without explicit knowledge of the CSI, as signal squaring and averaging performed over receive antennas provides a good estimate of the channel energy [7]. Besides, for general channel fading statistics, the performance of ED-based non-coherent SIMO communication is the same, in a scaling law sense, as that of the coherent scheme with perfect CSI [8].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Modulation for ED-Based Non-Coherent Massive SIMO Systems

Chang¹,

2021

IEEE Access

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Link adaptation in coherent wireless communications has sparked great research enthusiasm. However, adaptive techniques are equally important for non-coherent counterparts although relatively less attention has been dedicated to. This letter studies the adaptive modulation for the energy detection (ED)based non-coherent systems. Specifically, we consider a massive single-input multiple-output (SIMO) network, where a single-antenna user communicates with a base station (BS) with tens/hundreds of antennas. In particular, an optimization problem is formulated in order to find the signal-to-noise ratio (SNR) switching thresholds that maximize the average spectral efficiency (ASE). After deriving the SNR distribution and symbol error rate (SER), this problem is solved by applying the Lagrangian method. With switching thresholds, rate and power adaptation are made based on the instantaneous SNR. Numerical results indicate for the proposed scheme, there exists a trade-off between the target SER and ASE. That is, the ASE falls if one wishes to reduce the target SER. Under this circumstance, mounting more antennas can avoid the loss in ASE.INDEX TERMS Non-coherent communications, massive SIMO systems, energy detection, SNR switching thresholds, average spectral efficiency, nonnegative PAM.

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Non-Coherent Massive MIMO Systems: A Constellation Design Approach

Cited by 30 publications

References 25 publications

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

Deep-Learning-Based Non-Coherent DPSK Multiple-Symbol Differential Detection in Single-User Massive MIMO Systems

Performance Analysis on Spatial MFSK Modulation with Energy Detection

Adaptive Modulation for ED-Based Non-Coherent Massive SIMO Systems

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