An efficient genetic algorithm for large-scale transmit power control of dense industrial wireless networks
Xu Gong, David Plets, Emmeric Tanghe, Toon De Pessemier, Luc Martens,, Wout Joseph

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.…
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Taxonomy
TopicsAdvanced MIMO Systems Optimization · Wireless Communication Networks Research · Energy Harvesting in Wireless Networks
