2017/07/02 by Bassem Khalfi, Khalfi, Bassem, Bechir Hamdaoui +5
Engineering · Medicine · #Advanced MRI Techniques and Applications #Electrical and Bioimpedance Tomography #FOS: Computer and information sciences #Information Theory (cs.IT) #Sparse and Compressive Sensing Techniques
paper · pdf · doi:10.48550/arxiv.1707.00324
openalex publication_date 2017/07/02 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Compressive sampling has great potential for making wideband spectrum sensing\npossible at sub-Nyquist sampling rates. As a result, there have recently been\nresearch efforts that leverage compressive sampling to enable efficient\nwideband spectrum sensing. These efforts consider homogenous wideband spectrum,\nwhere all bands are assumed to have similar PU traffic characteristics. In\npractice, however, wideband spectrum is not homogeneous, in that different\nspectrum bands could present different PU occupancy patterns. In fact, the\nnature of spectrum assignment, in which applications of similar types are often\nassigned bands within the same block, dictates that wideband spectrum is indeed\nheterogeneous. In this paper, we consider heterogeneous wideband spectrum, and\nexploit its inherent, block-like structure to design efficient compressive\nspectrum sensing techniques that are well suited for heterogeneous wideband\nspectrum. We propose a weighted \ℓ1-minimization sensing information\nrecovery algorithm that achieves more stable recovery than that achieved by\nexisting approaches while accounting for the variations of spectrum occupancy\nacross both the time and frequency dimensions. In addition, we show that our\nproposed algorithm requires a lesser number of sensing measurements when\ncompared to the state-of-the-art approaches.\n