An Investigation of Primary Transmitter Detection Techniques in Cognitive Radio Networks from Network Optimization Perspective

In this paper, we investigate the problem of primary user detection 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 fundamental performance tradeoffs of these techniques without being limited by existing cognitive radio software and hardware platforms. To proceed, we first identify several dimensions for designing primary transmitter detection techniques in cognitive radio networks, including transmitter side vs. receiver side detection, and collaborative vs. non-collaborative detection. We then formulate primary transmitter detection techniques along these dimensions using mixed-integer nonlinear programming (MINLP). Evaluation results show the benefits of using the proposed optimization framework to profile the fundamental characteristics of primary transmitter detection techniques, thus motivating future research along this direction.