Modeling and Comparison of Primary User Detection Techniques in Cognitive Radio Networks

In this paper, we investigate the problem of spectrum sensing in cognitive radio networks. Compared with related work that aims to propose techniques at different layers of the network protocol stack for detecting primary users, we aim to investigate the capabilities and limitations of different primary user detection techniques from the perspective of network optimization. The goal is to understand the fundamental performance tradeoffs of different primary user detection techniques without being limited by existing cognitive radio software and hardware platforms. To proceed, we first identify several dimensions for designing primary user detection techniques in cognitive radio networks, and then formulate primary user detection techniques using mixed- integer nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework for profiling the fundamental characteristics of primary user detection techniques.