A Fuzzy-Logic-Based Load Balancing Scheme for a Satellite-Terrestrial Integrated Network

With the development of communication systems, users are becoming more widely distributed and require higher speed networks. A satellite-terrestrial integrated network could provide seamless coverage for these users. In previous studies of load balancing, initial access and load balancing are decided on based on signal reception and are performed reactively after the overloading occurs, which may not work well in satellite-terrestrial integrated networks. Therefore, this paper proposes a fuzzy-logic-based load balancing scheme. In this scheme, a fuzzy evaluation metric to pre-evaluate the user's impact on overload is presented. The fuzzy logic system is constructed based on adaptive neuro fuzzy system, which takes the user's signal reception, speed and data requirement as inputs. Then, the fuzzy-logic- and reinforcement-learning-based access is proposed to give an access decision for all users in the network to prevent overloading. Due to the large dimensions of action space, the reinforcement learning model is trained by the proposed fuzzy, deep, deterministic policy gradient. Next, the fuzzy-logic-based offloading algorithm is proposed to balance load after overloading. A simulation platform is established to evaluate the performance. Simulation results indicate that the proposed scheme can ensure load balance for a longer time than base line schemes while ensuring data rate of users.