Distributed Compressed Spectrum Sensing via Cooperative Support Fusion

Abstract

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.

Spectrum Occupancy Reconstruction in Distributed Cognitive Radio Networks Using Deep Learning

“…There are, however, numerous works that have tried to alleviate the computational burden that comes with each SU acting as an FC. For example, the study by Song et al [13] where a novel support fusion-based distributed compressive spectrum sensing was proposed. In this technique, a local compressed reconstruction and adaptive learning of support knowledge among SUs was proposed.…”

Research on Unbalanced Sample Classification Algorithm Based on Redundant Data Elimination

“…To solve the above problems, this paper proposes a classification method of unlabeled unbalanced sample data in multi-user social networks based on redundant data elimination. Firstly, the subspace fusion filter detection model is used to filter redundant data and suppress anti-interference on the prior features of unlabeled unbalanced sample data of multi-user social network obtained by random sampling, and the clustering convergence characteristic parameters of unlabeled unbalanced sample data of multi-user social network are extracted by statistical average analysis and distributed fusion detection method of autocorrelation characteristics [7]. Then, the extracted classification target characteristic parameters are input into the SVM classifier, and the adaptive weight distribution control of SVM classification is carried out by combining with the dynamic learning algorithm, so as to realize the optimal classification of unlabeled unbalanced sample data in multi-user social networks.…”