Low Complexity, Pairwise Layered Tabu Search for Large Scale 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.

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Low-Complexity Transmit Antenna Selection in Large-Scale Spatial Modulation Systems

International Journal of Antennas and Propagation ◽

10.1155/2015/242945 ◽

2015 ◽

Vol 2015 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Peng Wei ◽

Lu Yin ◽

Yue Xiao ◽

Xu He ◽

Shaoqian Li

Keyword(s):

Computational Complexity ◽

System Performance ◽

Large Scale ◽

Antenna Selection ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Spatial Modulation ◽

Transmit Antenna Selection ◽

Input Multiple Output

Transmit antenna selection (TAS) is an efficient way for improving the system performance of spatial modulation (SM) systems. However, in the case of large-scale multiple-input multiple-output (MIMO) configuration, the computational complexity of TAS in large-scale SM will be extremely high, which prohibits the application of TAS-SM in a real large-scale MIMO system for future 5G wireless communications. For solving this problem, in this paper, two novel low-complexity TAS schemes, named as norm-angle guided subset division (NAG-SD) and threshold-based NAG-SD ones, are proposed to offer a better tradeoff between computational complexity and system performance. Simulation results show that the proposed schemes can achieve better performance than traditional TAS schemes, while effectively reducing the computational complexity in large-scale spatial modulation systems.

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A Robust Hybrid Iterative Linear Detector for Massive MIMO Uplink Systems

Symmetry ◽

10.3390/sym12020306 ◽

2020 ◽

Vol 12 (2) ◽

pp. 306 ◽

Cited By ~ 1

Author(s):

Mahmoud A. Albreem ◽

Mohammed H. Alsharif ◽

Sunghwan Kim

Keyword(s):

Computational Complexity ◽

Convergence Rate ◽

Massive Mimo ◽

High Performance ◽

Large Scale ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Detection Algorithm ◽

Initial Vector ◽

High Data

Fifth-generation (5G) communications system is commercially introduced by several mobile operators where sub-6 GHz bands are the backbone of the 5G networks. A large-scale multiple-input multiple-output (MIMO), or massive MIMO (mMIMO), technology has a major impact to secure high data rate, high spectral efficiency, and quality of service (QoS). It could also have a major role in the beyond-5G systems. A massive number of antennas seek advanced signal processing to detect and equalize the signal. However, optimal detectors, such as the maximum likelihood (ML) and maximum posterior (MAP), are not desirable in implementation due to extremely high complexity. Therefore, sub-optimum solutions have been introduced to obtain and guarantee enough balance between the performance and the computational complexity. In this paper, a robust and joint low complexity detection algorithm is proposed based on the Jacobi (JA) and Gauss–Seidel (GS) methods. In such iterative methods, the performance, complexity, and convergence rate are highly dependent on the initial vector. In this paper, initial solution is proposed by exploiting the benefits of a stair matrix to obtain a fast convergence rate, high performance, and low complexity. Numerical results show that proposed algorithm achieves high accuracy and relieve the computational complexity even when the BS-to-user-antenna ratio (BUAR) is small.

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

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-021-00725-7 ◽

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.

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

Electronics ◽

10.3390/electronics9050756 ◽

2020 ◽

Vol 9 (5) ◽

pp. 756 ◽

Cited By ~ 1

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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Improved Massive MIMO RZF Precoding Algorithm Based on Truncated Kapteyn Series Expansion

Information ◽

10.3390/info10040136 ◽

2019 ◽

Vol 10 (4) ◽

pp. 136 ◽

Cited By ~ 1

Author(s):

Xiaomei Xue ◽

Zhengquan Li ◽

Yongqiang Man ◽

Song Xing ◽

Yang Liu ◽

...

Keyword(s):

Computational Complexity ◽

Series Expansion ◽

Large Scale ◽

Mimo System ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Inverse Matrix ◽

Achievable Rate ◽

Input Multiple Output ◽

Kapteyn Series

In order to reduce the computational complexity of the inverse matrix in the regularized zero-forcing (RZF) precoding algorithm, this paper expands and approximates the inverse matrix based on the truncated Kapteyn series expansion and the corresponding low-complexity RZF precoding algorithm is obtained. In addition, the expansion coefficients of the truncated Kapteyn series in our proposed algorithm are optimized, leading to further improvement of the convergence speed of the precoding algorithm under the premise of the same computational complexity as the traditional RZF precoding. Moreover, the computational complexity and the downlink channel performance in terms of the average achievable rate of the proposed RZF precoding algorithm and other RZF precoding algorithms with typical truncated series expansion approaches are analyzed, and further evaluated by numerical simulations in a large-scale single-cell multiple-input-multiple-output (MIMO) system. Simulation results show that the proposed improved RZF precoding algorithm based on the truncated Kapteyn series expansion performs better than other compared algorithms while keeping low computational complexity.

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Low-complexity sparse-aware multiuser detection for large-scale MIMO systems

EURASIP Journal on Wireless Communications and Networking ◽

10.1186/s13638-021-01904-8 ◽

2021 ◽

Vol 2021 (1) ◽

Author(s):

Rong Ran ◽

Hayoung Oh

Keyword(s):

Large Scale ◽

Mimo Systems ◽

Minimum Mean Square Error ◽

Multiple Input Multiple Output ◽

Low Complexity ◽

Finite Alphabet ◽

Multiuser Detectors ◽

Input Multiple Output ◽

Error Detector ◽

Significant Performance

AbstractSparse-aware (SA) detectors have attracted a lot attention due to its significant performance and low-complexity, in particular for large-scale multiple-input multiple-output (MIMO) systems. Similar to the conventional multiuser detectors, the nonlinear or compressive sensing based SA detectors provide the better performance but are not appropriate for the overdetermined multiuser MIMO systems in sense of power and time consumption. The linear SA detector provides a more elegant tradeoff between performance and complexity compared to the nonlinear ones. However, the major limitation of the linear SA detector is that, as the zero-forcing or minimum mean square error detector, it was derived by relaxing the finite-alphabet constraints, and therefore its performance is still sub-optimal. In this paper, we propose a novel SA detector, named single-dimensional search-based SA (SDSB-SA) detector, for overdetermined uplink MIMO systems. The proposed SDSB-SA detector adheres to the finite-alphabet constraints so that it outperforms the conventional linear SA detector, in particular, in high SNR regime. Meanwhile, the proposed detector follows a single-dimensional search manner, so it has a very low computational complexity which is feasible for light-ware Internet of Thing devices for ultra-reliable low-latency communication. Numerical results show that the the proposed SDSB-SA detector provides a relatively better tradeoff between the performance and complexity compared with several existing detectors.

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