Compressive autonomous sensing (CASe) for wideband spectrum sensing

Hongjian Sun, Arumugam Nallanathan, Jing Jiang, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

12 Citations (Scopus)


Compressive spectrum sensing techniques present many advantages over traditional spectrum sensing approaches, e.g., low sampling rate, and reduced energy consumption. However, when the spectral sparsity level is unknown, there are two significant challenges. They are: 1) how to choose an appropriate number of measurements, and 2) when to terminate the greedy recovery algorithm. In this paper, a compressive autonomous sensing (CASe) framework is presented that gradually acquires the wideband signal using sub-Nyquist rate. Further, a sparsity-aware recovery algorithm is proposed to reconstruct the full spectrum while solving the problem of under-fitting or over-fitting. Simulation results show that the proposed system can not only reconstruct the spectrum using the appropriate number of measurements, but also considerably improve the recovery performance when compared with the existing approaches.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Communications (ICC)
ISBN (Electronic)978-1-4577-2051-2
ISBN (Print)978-1-4577-2052-9
Publication statusPublished - Jun 2012
EventICC 2012 - 2012 IEEE International Conference on Communications - Ottawa, ON, Canada
Duration: 10 Jun 201215 Jun 2012


ConferenceICC 2012 - 2012 IEEE International Conference on Communications


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