Integration of a Precolouring Matrix in the Random Demodulator model for improved Compressive Ppectrum Estimation

The random demodulator (RD) is a compressive sensing (CS) architecture for acquiring frequency sparse, bandlimited 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

A Cognitive Radio Compressive Sensing Framework

With the proliferation of wireless devices and services, allied with further significant predicted growth, there is an ever increasing demand for higher transmission rates. This is especially challenging given the limited availability of radio spectrum, and is further exacerbated by a rigid licensing regulatory regime. Spectrum however, is largely underutilized and this has prompted regulators to promote the concept of opportunistic spectrum access. This allows unlicensed secondary users to use bands which are licensed to primary users, but are currently unoccupied, so leading to more efficient spectrum utilization. A potentially attractive solution to this spectrum underutilisation problem is cognitive radio (CR) technology, which enables the identification and usage of vacant bands by continuously sensing the radio environment, though CR enforces stringent timing requirements and high sampling rates. Compressive sensing (CS) has emerged as a novel sampling paradigm, which provides the theoretical basis to resolve some of these issues, especially for signals exhibiting sparsity in some domain. For CR-related signals however, existing CS architectures such as the random demodulator and compressive multiplexer have limitations in regard to the signal types used, spectrum estimation methods applied, spectral band classification and a dependence on Fourier domain based sparsity. This thesis presents a new generic CS framework which addresses these issues by specifically embracing three original scientific contributions: i) seamless embedding of the concept of precolouring into existing CS architectures to enhance signal sparsity for CR-related digital modulation schemes; ii) integration of the multitaper spectral estimator to improve sparsity in CR narrowband modulation schemes; and iii) exploiting sparsity in an alternative, non-Fourier (Walsh-Hadamard) domain to expand the applicable CR-related modulation schemes. Critical analysis reveals the new CS framework provides a consistently superior and robust solution for the recovery of an extensive set of currently employed CR-type signals encountered in wireless communication standards. Significantly, the generic and portable nature of the framework affords the opportunity for further extensions into other CS architectures and sparsity domains