Optimizing the Focusing Performance of Non-ideal Cell-Free mMIMO Using Genetic Algorithm for Indoor Scenario

Ke Chen, Siavash Safapourhajari, Toon De Pesemier, Luc Martens, Wout Joseph, Yang Miao

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
109 Downloads (Pure)

Abstract

This paper proposes a genetic algorithm (GA) combined with ray tracer to generate a cell-free topology of massive MIMO (mMIMO) for the optimal focusing performance serving multiple users. The realistic hardware impairment, for instance the non-ideal power amplifier, is taken into account of the system modeling and topology optimization. To the best of our knowledge, this is the first attempt to apply GA in optimizing the hardware-impaired multi-user cell-free mMIMO. Although the demonstrated numerical analysis is for indoor scenario, the proposed approach is transferable for generic scenarios. In GA, the base station (BS) antennas’ placement is encoded with an adjusted binary matrix representation, which is straightforward for the subsequent genetic operations. The explored candidates by GA can evolve beyond the parents, where the fitness of individuals is evaluated dynamically via a ray tracer radio channel simulator. Compared to the traditional GA, our proposed GA can find better solutions with a faster convergence speed. The algorithm provides near-optimal results in experiments, applicable to generic environment with multiple mobile users and different signal-to-interference-plus-noise ratios.
Original languageEnglish
Pages (from-to)8832-8845
JournalIEEE Transactions on Wireless Communications
Volume21
Issue number10
Early online date3 May 2022
DOIs
Publication statusPublished - 1 Oct 2022

Fingerprint

Dive into the research topics of 'Optimizing the Focusing Performance of Non-ideal Cell-Free mMIMO Using Genetic Algorithm for Indoor Scenario'. Together they form a unique fingerprint.

Cite this