Non-ideal Data Problems in Cooperative Spectrum Sensing

Abstract

In recent years, as the development of wireless communication technologies and the increase in smart devices rapidly, the shortage of spectrum resources has become the important issue for future wireless communication systems. Cognitive Radio (CR) enabled spectrum utilization through Dynamic Spectrum Access (DSA) which allowing unlicensed users to access licensed spectrum without interference. Cooperative Spectrum Sensing (CSS) which is an important part of cognitive radio systems to improve the sensing reliability by allowing multiple nodes shared sensing results and made a joint decision. In practical scenarios, CSS systems always faced several non-ideal data issues which include noise uncertainty, data missing, transmission errors, hardware impairments, and synchronization problems. These problems will distort sensing statistics, to reduce the detection probability, increase false alarm probability, and reduce the benefit of gain from cooperative. Therefore, this paper investigates how non-ideal data affects CSS systems and reviews several representative methods, including adaptive threshold detection, robust detection methods, data recovery techniques, synchronization correction, and uncertainty-aware fusion. The comparison results indicate that robust and intelligent cooperative methods can provide better sensing performance than traditional energy detection method under practical non-ideal conditions, especially in terms of detection probability, false alarm control, and overall system robustness. In addition, this paper discusses potential future research directions for intelligent cooperative sensing and explores the emerging technology such as potential influence of 6G networks and IoT, which may influence the development of CSS system.