Antenna subset selection for massive MIMO systems: A trace-based sequential approach for sum rate maximization

Hanif, Muhammad; Yang, Hong-Chuan; Boudreau, Gary; Sich, Edward; Seyedmehdi, Hossein

doi:10.1109/jcn.2018.000022

Cited by 16 publications

(9 citation statements)

References 30 publications

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“…In [9], one more point of attention was studied in that it is accurate transmit-side CSI and a novel method called AGS technique that integrates user scheduling and antenna selection for following an additional minimization in feedback overhead was explained. The AGS was constructed to improve the channel besides choosing the optimal antennas on the basis of the optimal gain attained.…”

Section: Introductionmentioning

confidence: 99%

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

2020

JNACS

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Generally, Multiple-Input Multiple-Output (MIMO) improves radio communication with enhanced reliability and capacity. The transmitter and receiver side, since there is an occurrence of multiple antennas, the appropriate Transmit Antenna Selection (TAS) for obtaining effectual performance is still a difficult point. In this work, a TAS method in the LTE system by exploiting Self-Adaptive Particle Swarm Optimization (SAPSO) is proposed for enhancing the system performance. Moreover, it develops self adaptiveness in the PSO by deciding the ability enhancement attained by each candidate solution for the TAS issue goes after by updating the particle solution on the basis of the enhancement. The experimentation model considers both Rician and Rayleigh channel, for four antenna configurations such as 2×2, 3×2, 4×2 and 4×4. To the next of the experimentation, it evaluates the performance of SAPSO-TAS with conventional Genetic Algorithm (GA)-TAS, Artificial Bee Colony (ABC)-TAS, Firefly (FF)-TAS, PSO-TAS, and Grey Wolf Optimization (GWO)-TAS models.

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Section: Introductionmentioning

confidence: 99%

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

2020

JNACS

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“…In [1], an antenna selection technique has been reviewed with considering different criterion such that channel capac-ity and bit error rate seeking both cost and hardware complexity reduction and It has proven that the performance is improved with the increase of number of receive antennas. The main target is to improve the spectral and transmit-energy efficiency [1][2][3][4][5][6][7] and it is obtained by increasing the number of antennas and RF chains. However, system complexity and hardware energy consumption is increased with the reduced number of RF chains in the antenna selection process.…”

Section: Introductionmentioning

confidence: 99%

“…In this power-based antenna selection method for the measured channels, the performance close to the convex optimization scheme is achieved. The same objective was in [4], [7][8][9][10][11][12] but it used the norm-and-correlation-based selection algorithm with low complexity for energy efficiency maximization has been proposed. Selection metric considers the effect of the norm of each channel column and correlation between columns while attaining low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

“…Another point of interest has been studied in [4,9,[13][14][15] which is accurate transmit-side channel state information (CSI) and a new algorithm named antenna group scheduling (AGS) algorithm which combines antenna selection and user scheduling for pursuing a further reduction in feedback overhead has been proposed. The AGS was built to enhance channel along with selecting the best antennas based on the best gain obtained.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed antenna selection technique provides better and less computation. In [4], [18][19][20], a low complexity greedy user selection scheme with linear zero forcing pre-coding at the base station has been proposed. The complexity of the proposed algorithm is remarkably low i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Antenna Selection and Power Allocation in Massive MIMO

Selvam¹,

Vishvaksenan²

2019

RADIOENGINEERING

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This paper explores the massive multiple-input multiple-output (Ma-MIMO) communication system possessing large number of antennas at the basestation (BS) serving multiple user terminal (UT) in single cell configuration. Due the large number of antennas at the BS, the RF chains used is also increasing with the increase in the total power consumption of the system such as circuit power consumption, filter, mixer and digital to analog converter power consumption. The main aim of this paper is to reduce the transmit power consumption with the proposed antenna selection and power allocation approach. Initially, the equal power is allocated to users to find the optimal number of antenna selection. Then for the number of antennas selected the optimal power allocation is derived to users. An algorithm is proposed to iteratively find the antenna selection and power allocation. The simulation is done to evaluate the average data rate and to find the optimal transmit power of the system.

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Hybrid optimization assisted transmit antenna selection for massive MIMO technology

Singh

Raja

2023

Multimed Tools Appl

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Antenna subset selection for massive MIMO systems: A trace-based sequential approach for sum rate maximization

Cited by 16 publications

References 30 publications

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Antenna Selection and Power Allocation in Massive MIMO

Hybrid optimization assisted transmit antenna selection for massive MIMO technology

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