Energy efficiency enhancement in heterogeneous networks:a joint resource allocation approach

To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency（EE） in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks（Het Nets） by using a joint resource allocation approach is proposed. The Het Nets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency（SE） in the meantime.

Joint Access Point Selection and Resource Allocation in MEC-Assisted Network:A Reinforcement Learning Based Approach

A distributed reinforcement learning(RL)based resource management framework is proposed for a mobile edge computing(MEC)system with both latency-sensitive and latency-insensitive services.We investigate joint optimization of both computing and radio resources to achieve efficient on-demand matches of multi-dimensional resources and diverse requirements of users.A multi-objective integer programming problem is formulated by two subproblems,i.e.,access point(AP)selection and subcarrier allocation,which can be solved jointly by our proposed distributed RL-based approach with a heuristic iteration algorithm.The proposed algorithm allows for the reduction in complexity since each user needs to consider only its own selection of AP without knowing full global information.Simulation results show that our algorithm can achieve near-optimal performance while reducing computational complexity significantly.Compared with other algorithms that only optimize either of the two sub-problems,the proposed algorithm can serve more users with much less power consumption and content delivery latency.