scholarly journals Low-Complexity Soft-MIMO Detection Algorithm Based on Ordered Parallel Tree-Search Using Efficient Node Insertion

2012 ◽  
Vol 37A (10) ◽  
pp. 841-849
Author(s):  
Kilhwan Kim ◽  
Jangyong Park ◽  
Jaeseok Kim
Keyword(s):  
Low Complexity ◽  
Detection Algorithm ◽  
Tree Search ◽  
Mimo Detection ◽  
Parallel Tree Search

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 Low Complexity, Pairwise Layered Tabu Search for Large Scale MIMO Detection

2021 ◽  
Author(s):  
SOURAV CHAKRABORTY ◽  
Nirmalendu Bikas Sinha ◽  
Monojit Mitra
Keyword(s):  
Tabu Search ◽  
Upper Bound ◽  
Large Scale ◽  
Complexity Analysis ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Detection Performance ◽  
Mimo Detection

Abstract This paper presents a low complexity pairwise layered tabu search (PLTS) based detection algorithm for a large-scale multiple-input multiple-output (MIMO) system. The proposed algorithm can compute two layers simultaneously and reduce the effective number of tabu searches. A metric update strategy is developed to reuse the computations from past visited layers. Also, a precomputation technique is adapted to reduce the redundancy in computation within tabu search iterations. Complexity analysis shows that the upper bound of initialization complexity in the proposed algorithm reduces from O(Nt4) to O(Nt3). The detection performance of the proposed detector is almost the same as the conventional complex version of LTS for 64QAM and 16QAM modulations. However, the proposed detector outperforms the conventional system for 4QAM modulation, especially in 16x16 and 8x8 MIMO. Simulation results show that the per cent of complexity reduction in the proposed method is approximately 75% for 64x64, 64QAM and 85% for 64x64 16QAM systems to achieve a BER of 10-3. Moreover, we have proposed a layer-dependent iteration number that can further reduce the upper bound of complexity with minor degradation in detection performance.

Study of FCLSD Algorithm Performance Based on LTE System

2012 ◽  
Vol 195-196 ◽  
pp. 96-103
Author(s):  
Ke Wen Liu ◽  
Quan Liu
Keyword(s):  
High Performance ◽  
Hardware Implementation ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Sphere Decoding ◽  
Mimo Detection ◽  
Decoding Algorithm ◽  
Algorithm Performance ◽  
Simulation Results ◽  
Ber Performance

Soft-output complex list sphere decoding algorithm is a low-complexity MIMO detection algorithm and its BER performance approximates that of Maximum-Likelihood. However, it has a problem of not fixed complexity, and which make it very difficult to implement. To resolve this and try best to retain the advantages of the algorithm, a modified algorithmfixed complex list sphere decoding algorithm was proposed. Based on LTE TDD system, this paper studies the performance of the FCLSD algorithm. The simulation results show that: the BER performance of the FCLSD algorithm is close to that of the CLSD algorithm. However, when the number of antennas and modulation order increasing, the FCLSD algorithm is non-constrained of spherical radius and has fixed complexity. In addition, hardware implementation of the FCLSD algorithm could be carried out by parallel processing, thereby greatly reducing the algorithm complexity. So it is a high-performance algorithm of great potential.

scholarly journals A Very Low Complexity QRD-M MIMO Detection Based on Adaptive Search Area

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 756 ◽  
Author(s):  
Bong-seok Kim ◽  
Sang-Dong Kim ◽  
Dongjun Na ◽  
Kwonhue Choi
Keyword(s):  
Multiple Input Multiple Output ◽  
Low Complexity ◽  
Symbol Error Rate ◽  
Detection Algorithm ◽  
Qr Decomposition ◽  
Noise Power ◽  
Adaptive Search ◽  
Mimo Detection ◽  
Channel Condition ◽  
Search Area

We propose a low complexity QR decomposition (QRD)-M multiple input multiple output (MIMO) detection algorithm based on adaptive search area. Unlike the conventional QRD-M MIMO detection algorithm, which determines the next survivor path candidates after searching over the entire constellation points at each detection layer, the proposed algorithm adaptively restricts the search area to the minimal neighboring constellation points of the estimated QRD symbol according to the instantaneous channel condition at each layer. First, we set up an adaptation rule for search area using two observations that inherently reflect the instantaneous channel condition, that is, the diagonal terms of the channel upper triangle matrix after QR decomposition and Euclidean distance between the received symbol vector and temporarily estimated symbol vector by QRD detection. In addition, it is found that the performance of the QRD-M algorithm degrades when the diagonal terms of the channel upper triangle matrix instantaneously decrease. To overcome this problem, the proposed algorithm employs the ratio of each diagonal term and total diagonal terms. Moreover, the proposed algorithm further decreases redundant complexity by considering the location of initial detection symbol in constellation. By doing so, the proposed algorithm effectively achieves performance near to the maximum likelihood detection algorithm, while maintaining the overall average computation complexity much lower than that of the conventional QRD-M systems. Especially, the proposed algorithm achieves reduction of 76% and 26% computational complexity with low signal to noise ratio (SNR) and high SNR, compared with the adaptive QRD-M algorithm based on noise power. Moreover, simulation results show that the proposed algorithm achieves both low complexity and lower symbol error rate compared with the fixed QRD-M algorithms.

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