A Genetic Algorithm-Based Antenna Selection Approach for Large-but-Finite MIMO Networks

Makki, Behrooz; Ide, Anatole; Svensson, Tommy; Eriksson, Thomas; Alouini, Mohamed‐Slim

doi:10.1109/tvt.2016.2646139

Cited by 50 publications

(71 citation statements)

References 14 publications

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“…For the JASUS problem, the vectors ω and ω are subject to constraint functions (12) and (13), respectively. However, employing functions (32) and (33) with MIS to generate samples, but we cannot ensure that the samples meet the constraints (12) and (13). In order to ensure that the samples drawn from (32) and (33) meet the constraints (12) and (13), we propose a new projection strategy.…”

Section: Constraints For the Amcmc-based Jasus Problemmentioning

confidence: 99%

“…Different schemes were used in many types of research, such as hybrid precoding and spatial modulation, to reduce the cost of the hardware and the power consumption of the system [10]. One of the best schemes to solve this problem is to applying antenna selection [11][12][13] to decide optimal subset of BS transmit antennas for decreasing the required number of high pricey RF chains while decreasing the resulting network performance loss.…”

Section: Introductionmentioning

confidence: 99%

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A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Maimaiti

Chuai

Gao

et al. 2019

J Wireless Com Network

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The massive MIMO (multiple-input multiple-output) technology plays a key role in the next-generation (5G) wireless communication systems, which are equipped with a large number of antennas at the base station (BS) of a network to improve cell capacity for network communication systems. However, activating a large number of BS antennas needs a large number of radio-frequency (RF) chains that introduce the high cost of the hardware and high power consumption. Our objective is to achieve the optimal combination subset of BS antennas and users to approach the maximum cell capacity, simultaneously. However, the optimal solution to this problem can be achieved by using an exhaustive search (ES) algorithm by considering all possible combinations of BS antennas and users, which leads to the exponential growth of the combinatorial complexity with the increasing of the number of BS antennas and active users. Thus, the ES algorithm cannot be used in massive MIMO systems because of its high computational complexity. Hence, considering the trade-off between network performance and computational complexity, we proposed a low-complexity joint antenna selection and user scheduling (JASUS) method based on Adaptive Markov Chain Monte Carlo (AMCMC) algorithm for multi-cell multi-user massive MIMO downlink systems. AMCMC algorithm is helpful for selecting combination subset of antennas and users to approach the maximum cell capacity with consideration of the multi-cell interference. Performance analysis and simulation results show that AMCMC algorithm performs extremely closely to ES-based JASUS algorithm. Compared with other algorithms in our experiments, the higher cell capacity and near-optimal system performance can be obtained by using the AMCMC algorithm. At the same time, the computational complexity is reduced significantly by combining with AMCMC.

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Section: Constraints For the Amcmc-based Jasus Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Maimaiti

Chuai

Gao

et al. 2019

J Wireless Com Network

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“…REDE IMUNOLÓGICA ARTIFICIAL PARA PROBLEMAS COMBINATORIAIS -COBAINET Dada a característica combinatorial do problema a ser tratado e o grande espaço de soluções a ser explorado, uma possível abordagemé o uso de meta-heurísticas que permitam explorar este espaço com baixa complexidade computacional. Neste sentido, algumas pesquisas que utilizam AG mostraram resultados promissores (e.g., [3] e [4]). Contudo, dado o teorema no free lunch para otimização [7] e resultados encorajadores e superiores ao AG em problemas combinatoriais obtidos pela meta-heurística baseada na teoria de redes imunológicas artificiais, o concentration-based artificial-immune network -CobAiNet [6], decidiu-se pela avaliação desta técnica neste contexto de MIMO de larga escala e seleção de antenas, e sua comparação com a técnica ITES [5] que já demonstrava ser melhor que o AG.…”

Section: Seleção De Antenasunclassified

“…Uma possibilidadeé tratar o problema de seleção de antenas como um problema de otimização convexa aplicando relaxamento [1] [2], com o intuito de maximizar a soma das capacidades. Em [3] e [4] se utilizam de algoritmos genéticos (AG) para realizar a busca. Por fim, uma técnica de seleção de antenas denominada ITES -iterative searches [5]é aplicada fora de um contexto de MIMO de larga escala com o propósito de minimizar a taxa de erro de bit (bit error rate -BER, em inglês).…”

Section: Introductionunclassified

“…Por fim, uma técnica de seleção de antenas denominada ITES -iterative searches [5]é aplicada fora de um contexto de MIMO de larga escala com o propósito de minimizar a taxa de erro de bit (bit error rate -BER, em inglês). Tal técnica apresenta um bom compromisso entre complexidade computacional e desempenho, superior ao AG de [4] e próximoà busca exaustiva.…”

Section: Introductionunclassified

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Redução da complexidade em sistemas MIMO de larga escala com seleção de antena utilizando redes imunológicas artificiais

Fernandes¹,

Panazio²,

Canale³

et al. 2019

Anais De XXXVII Simpósio Brasileiro De Telecomunicações E Processamento De Sinais

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Resumo-A seleção de antenas permite reduzir o consumo energético e a complexidade de hardware, com baixa perda de desempenho. Contudo, trata-se de um problema combinatorial não polinomial, que rapidamente torna inviável a busca exaustiva pela soluçãoótima. A fim de reduzir a complexidade computacional e, ainda assim, alcançar um bom desempenho, propõese o uso do algoritmo meta-heurístico baseado na teoria de redes imunológicas artificiais para seleção de antenas em sistemas MIMO de larga escala ou massivo. São avaliados o desempenho e a complexidade computacional da técnica proposta em relação a diversos parâmetros do sistema e outra técnica equivalente. Os resultados mostram a superior efetividade da técnica proposta. Palavras-Chave-Seleção de antenas, MIMO de larga escala ou massivo, redes imunológicas artificiais.

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Resource allocation in sparse code multiple access‐based systems for cloud‐radio access network in 5G networks

Zavleh

Bakhshi

2020

Trans Emerging Tel Tech

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In this paper, downlink joint user association, codebook assignment and power allocation for a Sparse Code Multiple Access (SCMA)‐based in Cloud Radio Access Network (C‐RAN) are investigated. The main aim of this paper is to accomplish a resource allocation to achieve the maximum total sum rate subject to SCMA limitation, total available power in Radio Remote Head (RRH), fronthaul constraint, users association, user power constraint, and Quality of Service requirements for each user with low complexity. To solve the recommended problem an iterative algorithm considering Successive Convex Approximation is utilized. The main problem is separated into two subproblems of joint user association and codebook assignment and power allocation. By defining a new binary variable for user association and codebook assignment and a new constraint for the user association, the joint user association and codebook assignment subproblem becomes an Integer Linear Programming. Then user association and codebook assignment subproblem solved with the help of the CVX toolbox and MOSEK solver. Furthermore, to solve the power allocation subproblem, Successive Convex Approximation with Low Complexity is applied. Then the power allocation subproblem solved by subgradient method for the Lagrange dual problem. In the simulation results, the effectiveness of the recommended optimization algorithm and resource allocation method are shown and compared to Non‐Orthogonal Multiple Access and Orthogonal Frequency Division Multiple Access.

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A Genetic Algorithm-Based Antenna Selection Approach for Large-but-Finite MIMO Networks

Cited by 50 publications

References 14 publications

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

A low-complexity algorithm for the joint antenna selection and user scheduling in multi-cell multi-user downlink massive MIMO systems

Redução da complexidade em sistemas MIMO de larga escala com seleção de antena utilizando redes imunológicas artificiais

Resource allocation in sparse code multiple access‐based systems for cloud‐radio access network in 5G networks

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