Advanced Coverage Optimization Techniques for Small Cell Clusters

doi:10.1109/CC.2015.7224694

Advanced Coverage Optimization Techniques for Small Cell Clusters

2015-08-01

10

PRE

AI

DOI：10.1109/CC.2015.7224694

原文链接

原文求助

摘要

En 中文

Coverage optimization is a main challenge for small cell clusters which are considered to be a promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization (MPSO) is introduced to centralized optimization which employs particle swarm optimization (PSO) and introduces a heuristic power control scheme to accelerate the algorithm to search for the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory (DGT) which converges to Nash Equilibrium (NE). Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm (SA), reaching higher coverage ratio and throughput while with less iterations.

Keyword:

small cell cluster

coverage optimization

particle swarm optimization

game theory

Advanced Coverage Optimization Techniques for Small Cell Clusters

摘要

AI总结

期刊

机构

引用论文