A Compressive Sensing Assisted Massive SM-VBLAST System: Error Probability and Capacity Analysis

Xiao, Lixia; Xiao, Pei; Liu, Zilong; Yu, Wenjuan; Haas, Harald; Hanzo, Lajos

doi:10.1109/twc.2019.2960505

Cited by 30 publications

(15 citation statements)

References 49 publications

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“…Then we estimate the final result from N t ones, which can significantly reduce the complexity. [10] 2013 [11,12,14] 2014 [17] 2015 [19] Specifically, we first obtain the optimal constellation symbol s q (q = 1, ..., N t ) for each antenna index as…”

Section: Low-complexity Hl-ml Detectormentioning

confidence: 99%

“…transmitter and/or the receiver employs numerous antennas, which require numerous radio frequency (RF) chains, hence imposing a substantial implementation cost, energy consumption and signal processing complexity as well as pilot overhead. In order to simplify the massive MIMO structure, sparse RF chain based spatial modulation (SM) aided MIMO systems have been developed in [5]- [14], where the information bits are conveyed both by the activated transmit antenna (TA) indices as well as by the classic amplitude phase modulation (APM) symbols. Hence SM constitutes a promising low-cost massive MIMO solution for next generation wireless communications [5]- [14].…”

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confidence: 99%

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Differentially-Encoded Rectangular Spatial Modulation Approaches the Performance of Its Coherent Counterpart

Xiao

Ruan

et al. 2020

IEEE Trans. Commun.

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A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

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Section: Low-complexity Hl-ml Detectormentioning

confidence: 99%

mentioning

confidence: 99%

Differentially-Encoded Rectangular Spatial Modulation Approaches the Performance of Its Coherent Counterpart

Xiao

Ruan

et al. 2020

IEEE Trans. Commun.

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“…By using NOMA combined with multiple antenna structures, not only high-rate data transmission is provided but also the destructive effects caused by fading are minimized [2][3][4][5][6][7]. However, symbol detection is a crucial process to detect symbols coherently in wireless communication systems [8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Therefore, various algorithms such as zero-forcing (ZF), the vertical Bell Laboratories layered spice-time (VBLAST), and ML are used for symbol detection [8][9][10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Seyman¹

2022

Computer Systems Science and Engineering

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One of the most important methods used to cope with multipath fading effects, which cause the symbol to be received incorrectly in wireless communication systems, is the use of multiple transceiver antenna structures. By combining the multi-input multi-output (MIMO) antenna structure with non-orthogonal multiple access (NOMA), which is a new multiplexing method, the fading effects of the channels are not only reduced but also high data rate transmission is ensured. However, when the maximum likelihood (ML) algorithm that has high performance on coherent detection, is used as a symbol detector in MIMO NOMA systems, the computational complexity of the system increases due to higher-order constellations and antenna sizes. As a result, the implementation of this algorithm will be impractical. In this study, the backtracking search algorithm (BSA) is proposed to reduce the computational complexity of the symbol detection and have a good bit error performance for MIMO-NOMA systems. To emphasize the efficiency of the proposed algorithm, simulations have been made for the system with various antenna sizes. As can be seen from the obtained results, a considerable reduction in complexity has occurred using BSA compared to the ML algorithm, also the bit error performance of the system is increased compared to other algorithms.

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“…Therefore, the main issue is reducing energy consumption in 5G IoT networks. Compressive sensing (CS) [ 4 , 5 , 6 , 7 , 8 ] presents some novel data-gathering strategies to reduce energy consumption in networks. According to the spatial, temporal, or spatial–temporal correlation characteristics of sensory data of 5G IoT networks, CS technique is able to recover the original senor node readings from

nodes with the help of

CS measurements as long as the signal can be sparsely represented in a certain transform domain [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT Networks

Zhu

Zhuang

2021

Sensors

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One of the major concerns in 5G IoT networks is that most of the sensor nodes are powered through limited lifetime, which seriously affects the performance of the networks. In this article, Compressive sensing (CS) technique is used to decrease transmission cost in 5G IoT networks. Sparse basis is one of the important steps in the CS. However, most of the existing sparse basis-based method such as DCT (Discrete cosine transform) and DFT (Discrete Fourier Transform) basis do not capture data structure characteristics in the networks. They also do not take into consideration multi-resolution representations. In addition, some of sparse basis-driven methods exploit either spatial or temporal features, resulting in performance degradation of CS-based strategies. To address these challenging problems, we propose a novel spatial–temporal correlation basis algorithm (SCBA). Subsequently, an optimal basis algorithm (OBA) is provided considering greedy scoring criteria. To evaluate the efficiency of OBA, orthogonal wavelet basis algorithm (OWBA) by employing NS (Numerical Sparsity) and GI (Gini Index) sparse metrics is also presented. In addition, we discuss the complexity of the above three algorithms, and prove that OBA has low numerical rank. After experimental evaluation, we found that OBA is capable of the sparsest representing original signal compared to spatial, DCT, haar-1, haar-2, and rbio5.5. Furthermore, OBA has the low recovery error and the highest efficiency.

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A Compressive Sensing Assisted Massive SM-VBLAST System: Error Probability and Capacity Analysis

Cited by 30 publications

References 49 publications

Differentially-Encoded Rectangular Spatial Modulation Approaches the Performance of Its Coherent Counterpart

Differentially-Encoded Rectangular Spatial Modulation Approaches the Performance of Its Coherent Counterpart

Symbol Detection Based on Back Tracking Search Algorithm in MIMO-NOMA Systems

Highly Efficient Spatial–Temporal Correlation Basis for 5G IoT Networks

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