Groupwise Neighbor Examination for Tabu Search Detection in Large MIMO Systems

Nguyen, Nhan T.; Lee, Kyungchun

doi:10.1109/tvt.2019.2953635

Cited by 11 publications

(14 citation statements)

References 11 publications

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“…The goal of signal detection is to determine s from y . This can be achieved via classical detection schemes such as the optimal maximum likelihood, near-optimal sphere decoding (SD) [108], tabu search (TS) [109], [110], suboptimal linear zero-forcing (ZF), minimum mean square error (MMSE), and successive interference cancellation (SIC) receivers. Furthermore, interest in the development of ML-based detectors (MLDs) has recently been growing.…”

Section: A Fundamentals Of Signal Detectionmentioning

confidence: 99%

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Intelligent Radio Signal Processing: A Survey

et al. 2021

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Intelligent signal processing for wireless communications is a vital task in modern wireless systems, but it faces new challenges because of network heterogeneity, diverse service requirements, a massive number of connections, and various radio characteristics. Owing to recent advancements in big data and computing technologies, artificial intelligence (AI) has become a useful tool for radio signal processing and has enabled the realization of intelligent radio signal processing. This survey covers four intelligent signal processing topics for the wireless physical layer, including modulation classification, signal detection, beamforming, and channel estimation. In particular, each theme is presented in a dedicated section, starting with the most fundamental principles, followed by a review of up-to-date studies and a summary. To provide the necessary background, we first present a brief overview of AI techniques such as machine learning, deep learning, and federated learning. Finally, we highlight a number of research challenges and future directions in the area of intelligent radio signal processing. We expect this survey to be a good source of information for anyone interested in intelligent radio signal processing, and the perspectives we provide therein will stimulate many more novel ideas and contributions in the future.

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Section: A Fundamentals Of Signal Detectionmentioning

confidence: 99%

“…The computational complexity of the maximum-likelihood detector increases exponentially with N , which is prohibitive even for a small value of N . To overcome this challenge, near-optimal reduced-complexity detection schemes have been proposed, such as SD [108] and TS [109], [110].…”

Section: ) Optimal Maximum-likelihood Detectormentioning

confidence: 99%

Intelligent Radio Signal Processing: A Survey

et al. 2021

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“…Taking the natural log of (46) gives ln X R = ln X R + iθ (34) where ln X R and iθ are the real and imaginary parts. The magnitude is given by…”

Section: Proposed Hybrid Neumann Series Based Mmse Assisted Detector mentioning

confidence: 99%

“…Kernighan and Lin were the first to apply VDS to the travelling salesman problem and graph partitioning problem [32]. Another Tabu search based neighbourhood detection scheme is proposed in [34], however, it requires division of the neighbours into groups, finding groups' best neighbour and then groups' best neighbours are compared to determine the final best neighbour. This requires many steps and results in high computation complexity.…”

Section: Introductionmentioning

confidence: 99%

“…It uses SD for searching for a better solution. In [34], the main issue is complexity of finding the best neighbour. Our aim is to reduce the complexity of finding optimal neighbourhood, therefore, in our work, the proposed neighbourhood search algorithm decomposes the signal into its real and imaginary constituents and works on refined randomisation.…”

Section: Introductionmentioning

confidence: 99%

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Efficient hybrid Neumann series based MMSE assisted detection for 5G and beyond massive MIMO systems

et al. 2020

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Massive multiple-input multiple-output (M-MIMO) is one of the cutting edge technologies that provides significant improvement in throughput, coverage and spectral efficiency. The challenge with M-MIMO systems is to extract individual signals from the composite signal, thus making optimal detectors prohibitively complex. Recently, approximate message passing (AMP) and its variant detectors have gained substantial importance due to their decreased complexity and improved performance. However, AMP algorithm does not always converge. Non-linear detectors like vertical Bell-Labs layered spaceiime improve the bit error rate (BER) but with high complexity, whereas, linear minimum mean square error (MMSE) detector offers low complexity while compromising on BER performance. Neumann series based MMSE detectors further reduce MMSE computational complexity, however, the BER performance remains the same. In this work, the authors propose a hybrid Neumann series based MMSE detector which decomposes the detected signal into its constituent components and apply a neighbourhood selection algorithm on the obtained components thus improving the overall performance. Another contribution of this work is derivation of an offset value for optimised neighbourhood set selection that enables more accurate detection while further reducing computational complexity. Simulation results confirm that the proposed scheme outperforms aforementioned algorithms in terms of BER performance and computational complexity in a continuously changing Rayleigh channel.

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

Chakraborty

Sinha

Mitra

2022

Wireless Pers Commun

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This paper presents a low complexity pairwise layered tabu search (PLTS) based detection algorithm for a large-scale multiple-input multipleoutput (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(N 4 t ) to O(N 3 t ). 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 16 × 16 and 8 × 8 MIMO. Simulation results show that the per cent of complexity reduction in the proposed method is approximately 75% for 64 × 64, 64QAM and 85% for 64 × 64 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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Groupwise Neighbor Examination for Tabu Search Detection in Large MIMO Systems

Cited by 11 publications

References 11 publications

Intelligent Radio Signal Processing: A Survey

Intelligent Radio Signal Processing: A Survey

Efficient hybrid Neumann series based MMSE assisted detection for 5G and beyond massive MIMO systems

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

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