Energy Efficient Switch Policy for Small Cells

• 作 者: GAN Xiaoying WANG Luyang FENG Xinxin LIU Jing YU Hui ZHANG Zhizhong LIU Haitao
• 机 构: Department of Electrical Engineering,Shanghai Jiao Tong University,The State Key Laboratory of Integ
• 出 处: 中国通信
• 发表日期: 2015年01期
• 关键词: heterogeneous network small cell switch policy Q-learning

Switch policy is essential for small cells to properly serve variable number of users in an energy efficient way.However,frequently switching small cell base stations(SBSs) may increase the network operating cost,especially when there is an nonnegligible start-up energy cost.To this end,by observing the variety of user number,we focus on the design of a switch policy which minimize the cumulative energy consumption.A given user transmission rate is guaranteed and the capability of SBSs are limited as well.According to the knowledge on user number variety,we classify the energy consumption problem into two cases.In complete information case,to minimize the cumulative energy consumption,an offline solution is proposed according to critical segments.A heuristic algorithm for incomplete information case(HAIIC) is proposed by tracking the difference of cumulative energy consumption.The upper bound of the Energy Consumption Ratio(ECR) for HAIIC is derived as well.In addition,a practical Q-learning based probabilistic policy is proposed.Simulation results show that the proposed HAIIC algorithm is able to save energy efficiently.

Switch policy is essential for small cells to properly serve variable number of users in an energy efficient way. Host, frequently switching small cell base stations (SBSs) may increase the network operating cost, especially when there is an nonnegligible start-up energy cost. To this end, by observing the variety of user number, we focus on the design of a switch policy which minimize the cumulative energy consumption. A given user transmission rate is guaranteed and the capability of SBSs are limited as well. user number variety, we classify the energy consumption problem into two cases.In least information case, to minimize the cumulative energy consumption, an offline solution is proposed according to critical segments. A heuristic algorithm for incomplete information case (HAIIC) is proposed by tracking the difference of cumulative energy consumption. The upper bound of the Energy Consumption Ratio (ECR) for HAIIC is derived as well. In addition, a practical Q-le arning based probabilistic policy is proposed. Simulation results show that the proposed HAIIC algorithm is able to save energy efficiently.