An Efficient Cooperative Spectrum Sensing for Cognitive Wireless Sensor Networks

• Published in: IEEE access 2023, Vol.11, p.132544-132556
• Main Authors: Zhang, Bin, Wu, Jun, Su, Mingkun, He, Meilin, Wang, Cong, Zhang, Zhixuan, Dai, Mingyuan, Cao, Weiwei
• Format: Article
• Language: English
• Subjects: Algorithms; Binary codes; Binary data; Cognitive radio; cooperative spectrum sensing; data fusion; Data integration; Decision making; multi-bit quantization; Parameters; Quantization (signal); Radio spectrum management; selective reporting; Sensors; Signal to noise ratio; Throughput; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3336654

In the face of explosive growth of wireless data and electronic devices, cognitive radio (CR) technology is an effective solution to identify spectrum resources underutilized by the primary user (PU). Moreover, cooperative spectrum sensing (CSS) is the key function of CR technology to enhance the detection performance by exploiting spatial diversity via the observations of spatially located sensor nodes (SNs). However, in a classical cooperative paradigm, each SN is always required to report individual local decision about the PU status to the fusion center (FC) for the global decision-making. Such a static reporting model regardless of the surrounding environment is not conducive to achieving the cooperative gain and adapting to a large cognitive wireless sensor network (CWSN). In view of this, this paper proposes an efficient CSS to optimize the reporting time and the achievable throughput of CWSNs, in which consists of selective reporting, multi-bit quantization, data fusion, and dynamic parameter. First of all, SNs only report some necessary observations by means of selective reporting, and then encodes local observations into multi-bit binary codes and send them to the FC. Further, the received local observations are aggregated into a final decision about the PU status. To be specific, the bit number of binary data and the selective reporting range as a parameter are dynamically adjusted at each SN. At last, simulation results demonstrate that the proposed CSS algorithm significantly not only reduces reporting time, but also improves the achievable throughput of CWSNs while maintaining the CSS performance.