Energy-efficient cell-association bias adjustment algorithm for ultra-dense networks

In recent years, energy efficiency has become an important topic, especially in the field of ultra-dense networks (UDNs). In this area, cell-association bias adjustment and small cell on/off are proposed to enhance the performance of energy efficiency in UDNs. This is done by changing the cell association relationship and turning off the extra small cells that have no users. However, the variety of cell association relationships and the switching on/off of the small cells may deteriorate some users’ data rates, leading to nonconformance to the users’ data rate requirement. Considering the discreteness and non-convexity of the energy efficiency optimization problem and the coupled relationship between cell association and scheduling during the optimization process, it is difficult to achieve an optimal cell-association bias. In this study, we optimize the network energy efficiency by adjusting the cell-association bias of small cells while satisfying the users’ data rate requirement. We propose an energy-efficient centralized Gibbs sampling based cell-association bias adjustment (CGSCA) algorithm. In CGSCA, global information such as channel state information, cell association information, and network load information need to be collected. Then, considering the overhead of the messages that are exchanged and the implementation complexity of CGSCA to obtain the global information in UDNs, we propose an energy-efficient distributed Gibbs sampling based cell-association bias adjustment (DGSCA) algorithm with a lower message-exchange overhead and implementation complexity. Using DGSCA, we derive the updated formulas for calculating the number of users in a cell and the users’ SINR. We analyze the implementation complexities (e.g., computation complexity and communication com- plexity) of the proposed two algorithms and other existing algorithms. We perform simulations, and the results show that CGSCA and DGSCA have faster convergence speed, as well as a higher performance gain of the energy efficiency and throughput compared to other existing algorithms. In addition, we analyze the importance of the users’ data rate constraint in optimizing the energy efficiency, and we compare the energy efficiency performance of different algorithms with different number of small cells. Then, we present the number of sleeping small cells as the number of small cells increases.