Learning-Based Spectrum Sensing for Cognitive Radio Systems

This paper presents a novel pattern recognition approach to spectrum sensing in collaborative cognitive radio systems. In the proposed scheme, discriminative features from the received signal are extracted at each node and used by a classifier at a central node to make a global decision about the availability of spectrum holes for use by the cognitive radio network. Specifically, linear and polynomial classifiers are proposed with energy, cyclostationary, or coherent features. Simulation results in terms of detection and false alarm probabilities of all proposed schemes are presented. It is concluded that cyclostationary-based schemes are the most reliable in terms of detecting primary users in the spectrum, however, at the expense of a longer sensing time compared to coherent based schemes. Results show that the performance is improved by having more users collaborating in providing features to the classifier. It is also shown that, in this spectrum sensing application, a linear classifier has a comparable performance to a second-order polynomial classifier and hence provides a better choice due to its simplicity. Finally, the impact of the observation window on the detection performance is presented. 1. Introduction In the past few years, there have been remarkable developments in wireless communications technology leading to a rapid growth in wireless applications. However, this dramatic increase in wireless applications is severely limited by bandwidth scarcity. Traditionally, fixed spectrum assignments, in which frequency bands are statically assigned to licensed users are employed. The static spectrum allocation prevents from assigning vacant spectrum bands to new users and services. Further, spectrum occupancy measurements have shown that some licensed bands are significantly underutilized. For example, the Spectral Policy Task Force reported that radio channels are typically occupied 15% of the time [1]. Hence, the limitation in the available spectrum bands occurs mainly due the underutilization of available spectrum resulting from the inefficient static allocation techniques. This underutilization of available spectrum resources has led regulatory bodies to urge the development of dynamic spectrum allocation paradigms, called cognitive radio (CR) networks. A CR network senses the operating environment for vacant spectrum opportunities and dynamically utilize the available radio resources [2, 3]. In CR technology, unlicensed (secondary) users are allowed to share the spectrum originally assigned to licensed (primary) users. Hence, frequency