Work

Particle Swarm Optimization Enabled Parametric Mapping for Channel Model Substitution

Year: 2025

Type: article

Abstract: Channel model substitution (CMS) is a technique that aims to replace a computationally challenging channel model with a simpler substitute. This technique is powerful for rapid adaptive signal processing and closed-form performance analytics. The parametric mapping between an original channel model and its substitute determines the utility of CMS. In the past decades, the moment matching criterion has dominated for conducting parametric mapping, which, however, is heuristic and has been proven non-optimal. In this paper, we propose to utilize particle swarm optimization (PSO) to obtain optimal parametric mapping relations for a general CMS problem, regardless of the distributional forms of the original channel model and the substitute. Taking the CMS techniques for the lognormal shadowed channel model as examples, simulation results show that the PSO enabled parametric mapping approach is capable of converging to the global optima under diverse system configurations, making CMS computationally feasible.

Source: IEEE Signal Processing Letters

DOI: 10.1109/lsp.2025.3596509

Authors: Zhongli Wang, Shuping Dang, Haiqiang Chen, Chengzhong Li

Institutions: Beihua University, University of Bristol, Guangxi University

Language: English

Topic: Advanced Wireless Communication Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Guangxi Province, Guangxi Key Research and Development Program