A Range Expansion Based Utility Optimization Algorithm for Ultra Dense HetWNets

Aiming at the energy waste and co-channel interfere problem caused by the working small cells with sparse users under the scenario of ultra-dense heterogeneous wireless networks (HetWNets), in this paper, an energy saving algorithm with fast convergence is proposed by optimizing user association through cell range expansion and switching off rare-user small cells. Simulated Annealing is employed in combination with the Gibbs Sampling to get the optimal solution of the user association and switch judgment, which finally improves the energy utilization of the wireless network. Simulations show that searching for and switching off serviceless small cells for users can greatly improve the overall energy utility of small cells and improve the overall throughput of the system to a certain extent. By combining Gibbs Sampling with Simulated Annealing, the convergence speed of the optimal solution can be improved. The proposed energy-saving algorithm can effectively improve the energy utility of the system and reduce the complexity of computing in the cloud.