Optimized Transfer Learning For Wireless Channel Selection

Abstract

A key challenge facing any channel selection technique is the dynamic nature of wireless channels. To address this issue, reinforcement learning techniques have widely been used, e.g., contextual multi-armed bandit (CMAB) theory. In fact, prior works solved the problem at each individual node. However, they did not consider the cooperative learning techniques, e.g., transfer learning. In communication systems, the advantage of transfer learning comes with computation and communication costs. Therefore, the decision of transferring the knowledge between agents should be optimized. In this paper, we develop a model to evaluate the feasibility and optimality of transfer learning for CMAB-based channel selection in communication systems. To this end, we introduce a utility model for evaluating these economical aspects. Leveraging Best Approximation Theory, we propose a new similarity concept and a transfer rule applied in the context of channel selection. Experimental results show that Extra Action is an efficient technique for transfer learning in a channel selection regime. More importantly, our proposed utility and optimization model is shown to be a powerful framework for deciding when transfer learning is feasible, and when it is optimal.