A highly efficient algorithm for phased-array mmWave massive MIMO beamforming

• Main Authors: Althuwayb, Ayman Abdulhadi, Hashim, Fazirulhisyam, Liew, Jiun Terng, Khan, Imran, Jeong, Woo Lee, Affum, Emmanuel Ampoma, Ouahabi, Abdeldjalil, Jacques, Sébastien
• Format: Article
• Published: Tech Science Press 2021
• Online Access: http://psasir.upm.edu.my/id/eprint/95754/; https://www.techscience.com/cmc/v69n1/42724
• Institution: Universiti Putra Malaysia

With the rapid development of the mobile internet and the internet of things (IoT), the fifth generation (5G) mobile communication system is seeing explosive growth in data traffic. In addition, low-frequency spectrum resources are becoming increasingly scarce and there is now an urgent need to switch to higher frequency bands. Millimeter wave (mmWave) technology has several outstanding features—it is one of the most well-known 5G technologies and has the capacity to fulfil many of the requirements of future wireless networks. Importantly, it has an abundant resource spectrum, which can significantly increase the communication rate of a mobile communication system. As such, it is now considered a key technology for future mobile communications. MmWave communication technology also has a more open network architecture; it can deliver varied services and be applied in many scenarios. By contrast, traditional, all-digital precoding systems have the drawbacks of high computational complexity and higher power consumption. This paper examines the implementation of a new hybrid precoding system that significantly reduces both calculational complexity and energy consumption. The primary idea is to generate several sub-channels with equal gain by dividing the channel by the geometric mean decomposition (GMD). In this process, the objective function of the spectral efficiency is derived, then the basic tracking principle and least square (LS) techniques are deployed to design the proposed hybrid precoding. Simulation results show that the proposed algorithm significantly improves system performance and reduces computational complexity by more than 45% compared to traditional algorithms.