An efficient genetic algorithm for large-scale transmit power control of dense industrial wireless networks

TL;DR

This paper introduces GATPC, an efficient genetic algorithm designed for large-scale transmit power control in dense industrial wireless networks, effectively reducing interference and adapting coverage in complex environments.

Contribution

The paper presents a novel genetic algorithm with tailored initialization, crossover, mutation, and parallelization for large-scale TPC in industrial WLANs, incorporating a realistic shadowing model.

Findings

Achieves up to 37-times speedup in runtime

Effectively adapts coverage to environmental shadowing

Reduces interference in dense industrial WLANs

Abstract

The industrial wireless local area network (IWLAN) is increasingly dense, not only due to the penetration of wireless applications into factories and warehouses, but also because of the rising need of redundancy for robust wireless coverage. Instead of powering on all the nodes with the maximal transmit power, it becomes an unavoidable challenge to control the transmit power of all wireless nodes on a large scale, in order to reduce interference and adapt coverage to the latest shadowing effects in the environment. Therefore, this paper proposes an efficient genetic algorithm (GA) to solve this transmit power control (TPC) problem for dense IWLANs, named GATPC. Effective population initialization, crossover and mutation, parallel computing as well as dedicated speedup measures are introduced to tailor GATPC for the large-scale optimization that is intrinsically involved in this problem. In contrast to most coverage-related optimization algorithms which cannot deal with the prevalent shadowing effects in harsh industrial indoor environments, an empirical one-slope path loss model considering three-dimensional obstacle shadowing effects is used in GATPC, in order to enable accurate yet simple coverage prediction. Experimental validation and numerical experiments in real industrial cases show the promising performance of GATPC in terms of scalability to a hyper-large scale, up to 37-times speedup in resolution runtime, and solution quality to achieve adaptive coverage and to minimize interference.