Research On Sub-Nyquist Wideband Spectrum Sensing Technologies In Cognitive Raido Networks

Nowadays, wireless communication technologies have become an essential part of our daily life. A large amount of spectrum is needed for the ever increasing transmission rate. However, traditional fixed spectrum allocation strategies results in artificial spectrum scarcity and greatly limits the development of new wireless applications. The cognitive radio technology which allows the secondary users to sense the activities of the primary users and to share the unoccupied spectrum can deal with this issue very well and efficiently improve the spectrum utilization percentage. Spectrum sensing technology, the fundamental of cognitive radio and the first step to dynamic spectrum access,is of great value to study. Future wideband wireless communication are is soar need of wideband spectrum sensing. However, traditional Nyquist-based sensing requires high sampling rate which is out of ADC's ability. In this article, we study sub-nyquist sampling based wideband spectrum sensing which can efficiently decrease the sampling rate for wideband spectrum sensing.At first, we introduce the background and situation of wideband spectrum sensing technology in detail. From two aspects: nyquist spectrum sensing and sub-nyquist spectrum sensing, we discuss and analysis the related works.We propose SWSS(Sub-Nyquist Wideband Spectrum Sensing) taking advantage of sub-nyquist sampling using multiple sampling rates. In SWSS, we sample the wideband signal using multiple sampling rates and reconstruct the spectrum using the Chinese Remainder Theorem. The MATLAB simulations show that SWSS can greatly decrease the sampling rates with a more simple system architecture.To deal with the problem of inaccuracy caused by the dynamics of wireless signals, we propose an adaptive policy ASWSS(Adaptive Sub-Nyquist Spectrum Sensing).ASWSS can change the sampling rate and detection resolution according to the changes of SNR(Siganl to Noise Ratio) and spectrum occupancy. The MATLAB simulations show that ASWSS can efficiently improve the stability and accuracy of the system.