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Chicken Swarm Optimization Based Optimal Channel Allocation in Massive MIMO

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Abstract

Energy efficiency (EE) plays a significant role in the progress towards the Fifth-generation (5G) wireless communication networks. Massive multiple-input multiple-output (MIMO) is a viable concept for the 5G networks due to the greater SE and EE. In this work, a Channel Selection (CS) scheme is proposed by selecting the optimal channel using the Chicken Swarm Optimization (CSO) algorithm. A massive MIMO model is implemented by considering the SE and EE. The main objective is to optimize the beam-forming vectors and power allocation for all the users. The multi-objective function can be defined to develop an effective and robust design with balanced SE and EE. The objective function for generating the optimal beam forming vectors is satisfying the signal to interference-plus-noise ratio (SINR) constraints. Based on the channel characteristics, the CSO Algorithm is used to produce the beam-forming vectors and power distribution. A projection matrix with a channel estimating framework is created once the channel state information is predicted. The selection of the index sets in the iteration process provides the optimized channel. Data transmission is performed through the optimal channel. According to the comparison analysis, the suggested CS scheme offers superior SE and EE to the existing CS schemes.

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Data sharing is not applicable as no datasets were generated or analyzed during this study.

Code Availability

The code generated during this study is available with the corresponding author.

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Authors and Affiliations

  1. Department of ECE, Kamaraj College of Engineering & Technology, Madurai, Tamil Nadu, India

    S. Nisha Rani

  2. Department of ECE, Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India

    G. Indumathi

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  1. S. Nisha Rani
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Correspondence to S. Nisha Rani.

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Nisha Rani, S., Indumathi, G. Chicken Swarm Optimization Based Optimal Channel Allocation in Massive MIMO. Wireless Pers Commun 129, 2055–2077 (2023). https://doi.org/10.1007/s11277-023-10225-6

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