A Learning-Based Two-Stage Spectrum Sharing Strategy With Multiple Primary Transmit Power Levels

Abstract

Multi-parameter cognition in a cognitive radio network (CRN) provides a more thorough understanding of the radio environments, and could potentially lead to far more intelligent and efficient spectrum usage for a secondary user. In this paper, we investigate the multi-parameter cognition problem for a CRN where the primary transmitter (PT) radiates multiple transmit power levels, and propose a learning-based two-stage spectrum sharing strategy. We first propose a data-driven/machine learning based multi-level spectrum sensing scheme, including the spectrum learning (Stage I) and prediction (the first part in Stage II). This fully blind sensing scheme does not require any prior knowledge of the PT power characteristics. Then, based on a novel normalized power level alignment metric, we propose two prediction-transmission structures, namely periodic and non-periodic, for spectrum access (the second part in Stage II), which enable the secondary transmitter (ST) to closely follow the PT power level variation. The periodic structure features a fixed prediction interval, while the nonperiodic one dynamically determines the interval with a proposed reinforcement learning algorithm to

Spectrum Intelligent Radio: Technology, Development, and Future Trends

“…To remove the limitation of the existing work and achieve multi-level spectrum sensing with no or minimal prior information, we recently proposed a data-driven/ML based scheme [8]. It is fully blind in the sense that the SU does not require any prior knowledge of the power mode.…”