Heuristically Accelerated Reinforcement Learning for Dynamic Secondary Spectrum Sharing

This paper examines how flexible cellular system architectures and efficient spectrum management techniques can be used to play a key role in accommodating the exponentially increasing demand for mobile data capacity in the near future. The efficiency of the use of radio spectrum for wireless communications can be dramatically increased by dynamic secondary spectrum sharing; an intelligent approach that allows unlicensed devices access to those parts of the spectrum that are otherwise underutilized by the incumbent users. In this paper, we propose a heuristically accelerated reinforcement learning (HARL)-based framework, designed for dynamic secondary spectrum sharing in Long Term Evolution cellular systems. It utilizes a radio environment map as external information for guiding the learning process of cognitive cellular systems. System level simulations of a stadium temporary event scenario show that the schemes based on the proposed HARL framework achieve high controllability of spectrum sharing patterns in a fully autonomous way. This results in a significant decrease in the primary system quality of service degradation due to interference from the secondary cognitive systems, compared with a state-of-the-art reinforcement learning solution and a purely heuristic typical LTE solution. The spectrum sharing patterns that emerge by using the proposed schemes also result in remarkable reliability of the cognitive eNodeB on the aerial platform. Furthermore, the novel principle and the general structure of heuristic functions proposed in the context of HARL are applicable to a wide range of self-organization problems beyond the wireless communications domain.