  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.
