Unified compressive sensing paradigm for the random demodulator and compressive multiplexer architectures

A major challenge in spectrum sensing for cognitive radio (CR) applications is the very high sampling rates involved, which imposes significant demands on the signal acquisition technology. This has given impetus to applying compressive sensing (CS) as a sub-Nyquist sampling paradigm for CR-type wireless signals which exhibit sparsity in certain domains. CS architectures like the random demodulator (RD) and compressive multiplexer (CM) can be used for CR spectral sensing, though both are inherently restricted in terms of the signal classes they can effectively process. To address these limitations, this study presents two unified RD and CM-based CS architectures that seamlessly integrate precolouring and the multitaper spectral estimator into their respective structures to facilitate efficient sensing of both digitally modulated and narrowband signals, along with popular CR-access technologies like orthogonal frequency division multiplexing. A significant feature of these unified CS architectures is they do not require a priori knowledge of either the input signal or modulation scheme, while a tristate spectral classifier is introduced to afford notably enhanced spectrum access opportunities for unlicensed secondary users. A critical performance evaluation corroborates that both unified architectures demonstrate consistently superior CS results and robustness across a broad range of CR-type signals, modulations and access technologies.