Improved LAS Detector for MIMO Systems with Imperfect Channel State Information

Issa, Chihaoui; Ammari, Mohamed Lassaad; Chouinard, Jean‐Yves

doi:10.1109/pimrc.2018.8580710

Cited by 1 publication

(4 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To sustain speed exceeding 1 Gbps and ultra-low latency communications, massive multiple-input multiple-output (MIMO) transmission technology was applied in the 5th generation wireless systems, where hundreds of antennas at the base station (BS) were implemented to serve tens of single-antenna users [2,3]. However, the large number of antennas will pose challenges for the receiver to perform signal detection efficiently, and various detectors have been proposed to tackle this problem [4][5][6][7][8][9][10][11][12][13]. Inspired by machine learning and artificial intelligence, Chockalingam et al proposed a series of metaheuristic detectors such as likelihood ascent search detector [4,5], reactive taboo search detector [6], and belief propagation detector [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…However, the large number of antennas will pose challenges for the receiver to perform signal detection efficiently, and various detectors have been proposed to tackle this problem [4][5][6][7][8][9][10][11][12][13]. Inspired by machine learning and artificial intelligence, Chockalingam et al proposed a series of metaheuristic detectors such as likelihood ascent search detector [4,5], reactive taboo search detector [6], and belief propagation detector [7,8]. Those detectors in [4][5][6][7][8] are appealing for massive MIMO systems, which have a similarly large number of transmit and receive antennas.…”

Section: Introductionmentioning

confidence: 99%

“…Inspired by machine learning and artificial intelligence, Chockalingam et al proposed a series of metaheuristic detectors such as likelihood ascent search detector [4,5], reactive taboo search detector [6], and belief propagation detector [7,8]. Those detectors in [4][5][6][7][8] are appealing for massive MIMO systems, which have a similarly large number of transmit and receive antennas. However, they are computationally uncompetitive when the number of active users is much smaller than the number of BS antennas.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive iterative error recovery detection algorithm for uplink massive multiple‐input multiple‐output systems

Jiang

Hou

2021

IET Communications

View full text Add to dashboard Cite

In this paper, an error recovery (ER) detection based on the lattice reduction (LR) algorithm with adaptive iteration is presented for uplink massive multiple-input multiple-output (MIMO) systems to obtain an improved bit error rate (BER) performance, especially when the number of active users increases. In the proposed algorithm, as the result of LR preprocessing, a larger diagonally dominant index (DDI) is used to measure the diagonal dominance of gram matrix for the initialisation of the ER detection. In addition, the larger DDI could be further used to realise adaptive iteration, which helps to strike a trade-off between the BER performance and the computational complexity. Moreover, two properties of the DDI are derived, and the simulation results show that the larger DDI leads to better detection performance, and it could be widely applied to linear elimination and MIMO detection algorithms. How to cite this article: Wu W, Jiang X, Hou J Adaptive iterative error recovery detection algorithm for uplink massive multiple-input multiple-output systems IET Communications 2021; 1-9.

show abstract