Learning Multiple Primary Transmit Power Levels for Smart Spectrum Sharing

Abstract

Multi-parameter cognition in a cognitive radio network provides a potential avenue to more efficient spectrum usage. In this paper, we propose a two-stage spectrum sharing strategy, where the primary user operates with multiple transmit power levels. Different from the conventional approaches, our method does not require any prior knowledge of the primary transmitter (PT) power characteristics. In the first stage, we use a conditionally conjugate Dirichlet process Gaussian mixture model to capture the multi-level power characteristics inherent in the PT signals, and design a Bayesian inference method to infer the model parameters. In the second stage, we propose a secondary transmitter (ST) prediction-transmission method based on reinforcement learning, which adapts to the PT power variation and strike an excellent tradeoff between the secondary network throughput and the interference to the primary network. The simulation results show the effectiveness of the proposed strategy.