A Low-Complexity CLSIC-LMMSE-Based Multi-User Detection Algorithm for Coded MIMO Systems with High Order Modulation

2017 ◽  
Vol 11 (4) ◽  
Keyword(s):  
Mimo Systems ◽  
Low Complexity ◽  
Detection Algorithm ◽  
High Order ◽  
High Order Modulation

scholarly journals Mitigating the noisy solution impact of mixed Gibbs sampling detector in high-order modulation large-scale MIMO systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Alex M. Mussi ◽  
Taufik Abrão
Keyword(s):  
Gibbs Sampling ◽  
Large Scale ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
High Order ◽  
Mimo Detection ◽  
Input Multiple Output ◽  
Improved Performance ◽  
High Order Modulation

AbstractA neighborhood-restricted mixed Gibbs sampling (MGS)-based approach is proposed for low-complexity high-order modulation large-scale multiple-input multiple-output (LS-MIMO) detection. The proposed LS-MIMO detector applies a neighborhood limitation (NL) on the noisy solution from the MGS at a distance d — thus, named d-simplified MGS (d-sMGS) — in order to mitigate its impact, which can be harmful when a high-order modulation is considered. Numerical simulation results considering 64-QAM demonstrated that the proposed detection method can substantially improve the MGS algorithm convergence, whereas no extra computational complexity per iteration is required. The proposed d-sMGS-based detector suitable for high-order modulation LS-MIMO further exhibits improved performance × complexity tradeoff when the system loading is high, i.e., when $\frac {K}{N}\geq 0.75$ K N ≥ 0.75 . Also, with increasing the number of dimensions, i.e., increasing number of antennas and/or modulation order, a smaller restriction of 2-sMGS was shown to be a more interesting choice than 1-sMGS.

scholarly journals A Novel Iterative Discrete Estimation Algorithm for Low-Complexity Signal Detection in Uplink Massive MIMO Systems

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 980 ◽  
Author(s):  
Hui Feng ◽  
Xiaoqing Zhao ◽  
Zhengquan Li ◽  
Song Xing
Keyword(s):  
Computational Complexity ◽  
Massive Mimo ◽  
Mimo Systems ◽  
Estimation Algorithm ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Damping Factor ◽  
Mimo Detection ◽  
Discrete Estimation

In this paper, a novel iterative discrete estimation (IDE) algorithm, which is called the modified IDE (MIDE), is proposed to reduce the computational complexity in MIMO detection in uplink massive MIMO systems. MIDE is a revision of the alternating direction method of multipliers (ADMM)-based algorithm, in which a self-updating method is designed with the damping factor estimated and updated at each iteration based on the Euclidean distance between the iterative solutions of the IDE-based algorithm in order to accelerate the algorithm’s convergence. Compared to the existing ADMM-based detection algorithm, the overall computational complexity of the proposed MIDE algorithm is reduced from O N t 3 + O N r N t 2 to O N t 2 + O N r N t in terms of the number of complex-valued multiplications, where Ntand Nr are the number of users and the number of receiving antennas at the base station (BS), respectively. Simulation results show that the proposed MIDE algorithm performs better in terms of the bit error rate (BER) than some recently-proposed approximation algorithms in MIMO detection of uplink massive MIMO systems.

scholarly journals A Low-Complexity Data Detection Algorithm for Massive MIMO Systems

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39341-39351 ◽  
Author(s):  
Imran A. Khoso ◽  
Xiaoming Dai ◽  
M. Nauman Irshad ◽  
Ali Khan ◽  
Xiyuan Wang
Keyword(s):  
Massive Mimo ◽  
Mimo Systems ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Data Detection

scholarly journals Low-Complexity Soft-Output Signal Detection Based on Improved Kaczmarz Iteration Algorithm for Uplink Massive MIMO System

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1564
Author(s):  
Hebiao Wu ◽  
Bin Shen ◽  
Shufeng Zhao ◽  
Peng Gong
Keyword(s):  
Signal Detection ◽  
Output Signal ◽  
Mimo Systems ◽  
Mimo System ◽  
Minimum Mean Square Error ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Base Station ◽  
Matrix Inversion ◽  
Iteration Algorithm

For multi-user uplink massive multiple input multiple output (MIMO) systems, minimum mean square error (MMSE) criterion-based linear signal detection algorithm achieves nearly optimal performance, on condition that the number of antennas at the base station is asymptotically large. However, it involves prohibitively high complexity in matrix inversion when the number of users is getting large. A low-complexity soft-output signal detection algorithm based on improved Kaczmarz method is proposed in this paper, which circumvents the matrix inversion operation and thus reduces the complexity by an order of magnitude. Meanwhile, an optimal relaxation parameter is introduced to further accelerate the convergence speed of the proposed algorithm and two approximate methods of calculating the log-likelihood ratios (LLRs) for channel decoding are obtained as well. Analysis and simulations verify that the proposed algorithm outperforms various typical low-complexity signal detection algorithms. The proposed algorithm converges rapidly and achieves its performance quite close to that of the MMSE algorithm with only a small number of iterations.

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