Open Access

ARTICLE

A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks

Waleed Shahjehan¹, Abid Ullah¹, Syed Waqar Shah¹, Imran Khan¹, Nor Samsiah Sani², Ki-Il Kim^3,*

1 Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan
2 Center for Artificial Intelligence Technology, Faculty of Information Science & Technology, Universiti Kebangsaan, Kajang, 43000, Malaysia
3 Department of Computer Science and Engineering, Chungnam National University, Daejeon, 34134, Korea

* Corresponding Author: Ki-Il Kim. Email:

Computers, Materials & Continua 2022, 72(2), 2797-2810. https://doi.org/10.32604/cmc.2022.026185

Received 17 December 2021; Accepted 24 January 2022; Issue published 29 March 2022

Abstract

Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for the fifth-generation (5G) wireless networks. It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station. However, increasing the number of antennas requires a large number of radio frequency (RF) chains which results in high power consumption. In order to reduce the RF chain's energy, cost and provide desirable quality-of-service (QoS) to the subscribers, this paper proposes an energy-efficient hybrid precoding algorithm for mmWave massive MIMO networks based on the idea of RF chains selection. The sparse digital precoding problem is generated by utilizing the analog precoding codebook. Then, it is jointly solved through iterative fractional programming and successive convex optimization (SCA) techniques. Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.

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Keywords

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