Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization

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Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization

Chen, Z; Tang, J; Tang, H; Zhang, X; So, DKC; Wong, KK; (2021) Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization. In: ICC 2021 - IEEE International Conference on Communications. IEEE: Montreal, QC, Canada. Green open access

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Abstract

In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multi-objective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation algorithms.

Type:	Proceedings paper
Title:	Channel Estimation of IRS-Aided Communication Systems with Hybrid Multiobjective Optimization
Event:	ICC 2021 - IEEE International Conference on Communications
ISBN-13:	9781728171227
Open access status:	An open access version is available from UCL Discovery
DOI:	10.1109/ICC42927.2021.9500433
Publisher version:	https://doi.org/10.1109/ICC42927.2021.9500433
Language:	English
Additional information:	This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords:	Training, Measurement errors, Surface waves, Simulation, Channel estimation, Evolutionary computation, Linear programming
UCL classification:	UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng
URI:	https://discovery.ucl.ac.uk/id/eprint/10136409