A Novel Matrix Optimization for Compressive Sampling-Based Sub-Nyquist OFDM Receiver in Cognitive Radio

摘要

Modulated wideband converter is the most commonly accepted technique for implementing sub-Nyquist compressive sampling-based wireless receiver to reduce the analog and digital processing complexity when detecting wideband spectrum for cognitive radio systems. However, the issue of non-optimal mutual coherence, which leads to a higher receiving bit error rate, has not been considered in existing compressive sampling-based cognitive radio studies. Furthermore, existing theoretical compressive sampling-based solutions cannot be directly applied because typical modulated wideband converter-based designs use fixed parameters that cannot be easily updated during their sampling operations. This paper presents a novel matrix optimization which can be incorporated into modulated wideband converter-based cognitive radio to enhance its detection accuracy for OFDM signals. The proposed approach can also be predetermined to reduce the computation complexity, while remains compatible with standard digital OFDM receiver's operation. Simulation results show that our proposed system can consistently produce smaller compressive sampling reconstruction error in terms of lower bit error rate under various operating conditions compared to existing systems.