Huang Jijun

A novel decentralized scheme for cooperative compressed spectrum sensing in distributed networks

Compressed sensing (CS) recently turns out to be an effective approach to alleviate the sampling bottleneck in wideband spectrum sensing. However, the computation overhead incurred by compressed reconstruction is nontrivial, especially in a power-constrained cognitive radio (CR). Moreover, additional information, which is generally unavailable in practice, is needed in conventional CS-based wideband spectrum sensing schemes to improve the reconstruction quality as well as the detection performance. To address these issues, this paper proposes a novel decentralized scheme for cooperative compressed spectrum sensing in distributed CR networks. Our key observation is that the sparse signals are unnecessary to be reconstructed since the task of spectrum sensing is only interested in the spectrum occupancy status. The major novelty of the proposed scheme relates to the use of Karcher mean as a statistic indicating the spectrum occupancy status, thereby eliminating the compressed reconstruction stage and significantly reducing the computational complexity. Considering limited communication resources per CR, a decentralized implementation based on alternating direction method of multipliers is presented to calculate the Karcher mean via one-hop communications only. The superior performance of the proposed scheme is demonstrated by comparing with several existing decentralized schemes in terms of detection performance, communication overhead, and computational complexity.

Distributed Compressed Spectrum Sensing via Cooperative Support Fusion

Spectrum sensing in wideband cognitive radio (CR) networks faces several significant practical challenges, such as extremely high sampling rates required for wideband processing, impact of frequency-selective wireless fading and shadowing, and limitation in power and computing resources of single cognitive radio. In this paper, a distributed compressed spectrum sensing scheme is proposed to overcome these challenges. To alleviate the sampling bottleneck, compressed sensing mechanism is used at each CR by utilizing the inherent sparsity of the monitored wideband spectrum. Specifically, partially known support (PKS) of the sparse spectrum is incorporated into local reconstruction procedure, which can further reduce the required sampling rate to achieve a given recovery quality or improve the quality given the same sampling rate. To mitigate the impact of fading and shadowing, multiple CRs exploit spatial diversity by exchanging local support information among them. The fused support information is used to guide local reconstruction at individual CRs. In consideration of limited power per CR, local support information percolates over the network via only one-hop local information exchange. Simulation results testify the effectiveness of the proposed scheme by comparing with several existing schemes in terms of detection performance, communication load, and computational complexity. Moreover, the impact of system parameters is also investigated through simulations.