Spectrum inpainting: a new framework for spectrum status determination in large cognitive radio networks

Authors:
Paul Potier;Calynna Sorrells;Yonghui Wang;Lijun Qian;Husheng Li
Affiliations:
Prairie View A&M University, Prairie View, USA 77446 and Texas A&M University System, College Station, USA 77840;Prairie View A&M University, Prairie View, USA 77446 and Texas A&M University System, College Station, USA 77840;Prairie View A&M University, Prairie View, USA 77446 and Texas A&M University System, College Station, USA 77840;Prairie View A&M University, Prairie View, USA 77446 and Texas A&M University System, College Station, USA 77840;University of Tennessee, Knoxville, USA 37996
Venue:
Wireless Networks
Year:
2014

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Abstract

In this paper, the problem of spectrum status determination is considered for large cognitive radio (CR) ad hoc networks. Spectrum sensing and spectrum decision are critical for cognitive radio network throughput and hence obtaining accurate knowledge of the spectrum status is vitally important to better spectrum usage decisions. The major challenge of this type of problem lies in the fact that for a network covering a large geographical area, only very limited measurements of spectrum occupancy during spectrum sensing may be obtained by the CR users for a certain location in any given time slot. This is due to both the hardware limitations as well as the tradeoff between spectrum sensing time and data throughput of the CR users. By representing the spectrum sensing results across the network as an image, spectrum status determination is formulated as an image recovery problem. The method of total variation inpainting is applied to solve the problem with low determination error. The proposed method takes advantage of the correlations in multiple dimensions and the numerical results demonstrate the effectiveness of the proposed scheme.