A Machine Learning Algorithm for Unlicensed LTE and WiFi Spectrum Sharing

Shared use of unlicensed spectrum in practice for coexistence with WiFi is rather complex and to achieve optimum usage can be highly challenging. While maximal utilization is desirable for unlicensed LTE, it is essentially important not to disturb WiFi activity in the unlicensed channel when designing a coexistence scheme. Opportunistic exploitation of idle gaps, or white spaces, in the WiFi channel for unlicensed LTE transmissions enables achieving the above objectives. However, complex analytical approaches to the opportunistic coexistence problem require considerable computation and might result in excessive latency, which would be undesirable. Machine learning schemes may reduce the computational complexity and hence not only reduce the latency but also help with energy consumption in wireless communications systems. We propose a novel algorithm based on reinforcement learning technique for the problem of opportunistic coexistence of unlicensed LTE and WiFi. The proposed approach in particular is based on Q-Learning, which provides a robust and model-free decision-making framework that enables online and distributive coexistence of small cells with WiFi. Our approach takes into account the latency imposed on WiFi activity by employing carrier sensing at the base station, and aims to minimize it, while maximizing unlicensed LTE utilization of the idle spectral resources.