Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small ce...Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are 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 tbr 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.展开更多
To establish wireless channel suitable for the cabin environment, the power coverage was investigated with distributed antenna system and centralized antenna system based on the actual measurement of channel impulse r...To establish wireless channel suitable for the cabin environment, the power coverage was investigated with distributed antenna system and centralized antenna system based on the actual measurement of channel impulse response. The results indicated that the distributed antenna system has more uniform power coverage than the centralized antenna system. The average relative errors of receiving power of both antennas were calculated. The optimal position of the centralized antenna was obtained by Gaussian function refinement, making the system achieve a better transmission power with the same coverage effect, and providing a reference for antenna location in the future real communication in the cabin.展开更多
基金supported by the National High-Tech Development 863 Program of China (Grant DOS. 2012AA012801)National Natural Science Foundation of China(No.61331009)
文摘Coverage challenge for small considered to be a optlmlzation is a main cell clusters which are 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 tbr 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.
基金Supported by the National High Technology Research and Development Program of China("863"ProgramNo.2009AA011507)+2 种基金National Science and Technology Major Project of the Ministry of Science and Technology of China(No.2009ZX03007-003)National Natural Science Foundation of China(No.61101223)Ph.D.Programs Foundation of Ministry of Education of China(No.20110032120087 and No.20110032110029)
文摘To establish wireless channel suitable for the cabin environment, the power coverage was investigated with distributed antenna system and centralized antenna system based on the actual measurement of channel impulse response. The results indicated that the distributed antenna system has more uniform power coverage than the centralized antenna system. The average relative errors of receiving power of both antennas were calculated. The optimal position of the centralized antenna was obtained by Gaussian function refinement, making the system achieve a better transmission power with the same coverage effect, and providing a reference for antenna location in the future real communication in the cabin.