Accelerated Coverage Optimization With Particle Swarm in the Quotient Space Characterizing Antenna Azimuths of Cellular Networks

In order to provide a reliable connection for the ever-increasing devices, cellular networks are facing critical challenges, among which is the issue of network coverage optimization. The costly objective function for network coverage appeals for an efficient approach of optimization, as the canonical particle swarm optimization (PSO) suffers excessive computation caused by population and iteration. The specificity of the antenna azimuths in cellular networks is hereby investigated so as to construct the corresponding metric structures in the naive antenna azimuth space and its quotient space. We introduce two accelerated PSO algorithms induced by the metric structures to maximize the cellular network coverage ratio. The former, named aPSO/S, only considers the shortest arc distance in the naive space, whereas the latter, named aPSO/QS, considers both the shortest arc distance and the equivalence among solutions in the quotient space. The proposed PSO algorithms, mainly the latter as referred to, not only guide the particles to fly toward the shortest arc distance directions but also eliminate lots of unnecessary calculation caused by the equivalence among solutions. The experiments show that the proposed aPSO/QS algorithm edge outs the canonical algorithm and its typical derivatives with various boundary conditions from the perspective of either the convergence speed or the stability with the hyper-parameters. To reach the same performance as that of its best competitor, the proposed aPSO/QS algorithm needs only a fraction of the computation cost so as to be applied in the efficiency-aware live network optimization.