2018/09/16 by Himani Joshi, Sumit J. Darak, Joshi, Himani +5
Engineering · #Sparse and Compressive Sensing Techniques #Advanced Electrical Measurement Techniques #Advanced Adaptive Filtering Techniques
paper · pdf · doi:10.48550/arxiv.1809.05826
In this paper, we consider non-contiguous wideband spectrum sensing (WSS) for\nspectrum characterization and allocation in next generation heterogeneous\nnetworks. The proposed WSS consists of sub-Nyquist sampling and digital\nreconstruction to sense multiple non-contiguous frequency bands. Since the\nthroughput (i.e. the number of vacant bands) increases while the probability of\nsuccessful reconstruction decreases with increase in the number of sensed\nbands, we develop an online learning algorithm to characterize and select\nfrequency bands based on their spectrum statistics. We guarantee that the\nproposed algorithm allows sensing of maximum possible number of frequency bands\nand hence, it is referred to as throughput optimized WSS. We also provide a\nlower bound on the number of time slots required to characterize spectrum\nstatistics. Simulation and experimental results in the real radio environment\nshow that the performance of the proposed approach converges to that of Myopic\napproach which has prior knowledge of spectrum statistics.\n