Nonuniform Sampling Pattern Design for Compressed Spectrum Sensing in Mobile Cognitive Radio Networks

Compressed spectrum sensing (CSS) plays a pivotal role in dynamic spectrum access within mobile cognitive radio networks by offering reduced power consumption and lower hardware costs. The multicoset sampler, a well-known implementation for periodic nonuniform sampling, has been widely studied and is considered a promising architecture for realizing CSS. This article focuses on the design of the multicoset sampling pattern, aiming at enhancing the isometry property of the sensing matrix. Unlike previous studies which assume a noise-free setup, our work considers the problem in a real-world environment with noise. First, we propose a deterministic algorithm for sampling pattern generation, particularly for specific hardware setup parameters. This algorithm offers strict mutual-coherence control in the multicoset sensing matrix. To address more general hardware configurations, we propose two optimization algorithms. One of them searches for nearly optimal sampling patterns through a random search strategy, while the other employs a greedy pursuit strategy to find a local optimizer. Furthermore, we propose an algorithm to iteratively optimize the sampling pattern between consecutive spectrum sensing windows by minimizing a restricted version of mutual coherence. The excellent performance of our proposed algorithms has been demonstrated through numerical experiments and has been verified on a self-developed hardware platform.