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

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 paper 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

A Multitaper-Random Demodulator Model for Narrowband Compressive Spectral Estimation

The random demodulator (RD) is a compressive sensing (CS) system for acquiring and recovering bandlimited sparse signals, which are approximated by multi-tones. Signal recovery employs the discrete Fourier transform based periodogram, though due to bias and variance constraints, it is an inconsistent spectral estimator. This paper presents a Multitaper RD (MT-RD) architecture for compressive spectrum estimation, which exploits the inherent advantage of the MT spectral estimation method from the spectral leakage perspective. Experimental results for sparse, narrowband signals corroborate that the MT-RD model enhances sparsity so affording superior CS performance compared with the original RD system in terms of both lower power spectrum leakage and improved input noise robustness

»A French Music of France«

Abstract – One of the major challenges in cognitive radio (CR) networks is the need to sample signals as efficiently as possible without incurring the loss of vital information. Compressive Sensing (CS) is a new sampling paradigm which provides a theoretical framework for sub-sampling signals which are characterized as being sparse in the frequency domain. The random demodulator (RD) is a CS-based architecture which has been employed to acquire frequency sparse, bandlimited signals which typify the signals which often occur in many CR-related applications. This paper investigates the impact of precolouring upon CS performance by combining the RD with an autoregressive (AR) filter model to enhance compressive spectral estimation. Quantitative results with quadrature phased shift keying (QPSK) modulated multiband signals, corroborate that adopting a precolouring strategy both reduces the spectral leakage in the power spectrum, and concomitantly improves the overall signal-to-noise ratio (SNR) performance of the compressive spectrum estimator