Integration of a precolouring matrix in the random demodulator model for improved compressive spectrum estimation

The random demodulator (RD) is a compressive sensing (CS) architecture for acquiring frequency sparse, ban-dlimited signals. Such signals occur in cognitive radio networks for instance, where efficient sampling is a critical design requirement. A recent RD-based CS system has been shown to effectively acquire and recover frequency sparse, high-order modulated multiband signals which have been precoloured by an autoregressive (AR) filter. A shortcoming of this AR-RD architecture is that precolouring imposes additional computational cost on the signal transmission system. This paper introduces a novel CS architecture which seamlessly embeds a precolouring matrix (PM) into the signal recovery stage of the RD model (iPM-RD) with the PM depending only upon the AR filter coefficients, which are readily available. Experimental results using sparse wideband quadrature phased shift keying (QPSK) and 64 quadrature amplitude modulation (64QAM) signals confirm the iPM-RD model provides improved CS performance compared with the RD, while incurring no performance degradation compared with the original AR-RD architecture.