Improved joint antenna selection and user scheduling for massive MIMO systems

Dong, Yuhan; Tang, Yuanyuan; Shenzhen, Kai Zhang

doi:10.1109/icis.2017.7959971

Cited by 15 publications

(17 citation statements)

References 13 publications

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“…Antenna Selection norm-and-correlation-based se-lection algorithm with low complexity for energy efficiency maximization [4], [7][8][9][10][11][12] transmit-side channel state information (CSI) used for antenna selection and user scheduling to reduce feedback overhead [4,9,[13][14][15] PSO, GA, and ABC used and compared and they are used to achieve better antenna selection with higher capacity and less CPU time [1], [16].…”

Section: Work Area Key Pointsmentioning

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%

“…The objective is to maximize the data rates. The optimal solution is a highly complex exhaustive brute force search (BFS) [10], [18] over all possible combinations of antennas and users. The proposed algorithm aims to maximize the achievable sum-rate and to make use of both the spatial selectivity gain and multiuser diversity gain offered by the antenna selection and user scheduling, respectively.…”

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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Section: Work Area Key Pointsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Antenna Selection and Power Allocation in Massive MIMO

Selvam¹,

Vishvaksenan²

2019

RADIOENGINEERING

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“…The purpose is to exploit data rates. The best solution is an extremely complex comprehensive BFS [14], overall likely amalgamations of users and antennas.…”

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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“…In these algorithms, users contributing insignificantly to system sum-rate of massive MIMO network, where users are removed iteratively. A swapping based and simplified JASUS algorithm is suggested by Dong et al [14] for downlink massive MIMO network with ZF precoding improving the system sum-rate and reducing the computational complexity. A low complexity JASUS algorithm with optimal system sum-rate growth is reported by Benmimoune et al [15] where the users and antennas are removed step by step method.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Massive Multi-input and Multi-output with Imperfect Channel State Information

Sheikh¹,

Bora²,

Hussain³

2019

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The type and state of the fading channel directly affect the performance of massive multi-input and multi-output (MIMO) system. For example, the small and large fading (SSF and LSF) of the channel have a great impact on the sum-rate of the system. However, the channel state information (CSI) is far from perfect, making it difficult to analyze the sum-rate of massive MIMO systems with uniform user distribution. To solve the problem, this paper proposes three scheduling algorithms, namely, semi-orthogonal user scheduling (SUS), random user scheduling (RUS), and distance-dependent user scheduling (DUS). The three algorithms were adopted to schedule different number of users (8, 10 and 12), based on the maximum signalto-noise ratio (SNR) with changing number of base station antennas, number of active users, etc. The zero forcing (ZF) precoding was employed to improve the sum-rate, and the highly scattering Rayleigh fading channel was considered for both SSF and LSF, in the light of user locations. Under imperfect CSI and additional noise, the DUS achieved higher sum-rate than the other algorithms. The research results shed new light on the use of massive MIMO systems for 5G applications with high sum-rate requirements.

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Improved joint antenna selection and user scheduling for massive MIMO systems

Cited by 15 publications

References 13 publications

Antenna Selection and Power Allocation in Massive MIMO

Antenna Selection and Power Allocation in Massive MIMO

Self-adaptive Particle Swarm Optimization for Optimal Transmit Antenna Selection

Performance Analysis of Massive Multi-input and Multi-output with Imperfect Channel State Information

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