951 research outputs found
Recommended from our members
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
Recommended from our members
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
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
AnalogâtoâDigital Conversion for Cognitive Radio: Subsampling, Interleaving, and Compressive Sensing
This chapter explores different analog-to-digital conversion techniques that are suitable to be implemented in cognitive radio receivers. This chapter details the fundamentals, advantages, and drawbacks of three promising techniques: subsampling, interleaving, and compressive sensing. Due to their major maturity, subsampling- and interleaving-based systems are described in further detail, whereas compressive sensing-based systems are described as a complement of the previous techniques for underutilized spectrum applications. The feasibility of these techniques as part of software-defined radio, multistandard, and spectrum sensing receivers is demonstrated by proposing different architectures with reduced complexity at circuit level, depending on the application requirements. Additionally, the chapter proposes different solutions to integrate the advantages of these techniques in a unique analog-to-digital conversion process
Wideband Power Spectrum Sensing: a Fast Practical Solution for Nyquist Folding Receiver
The limited availability of spectrum resources has been growing into a
critical problem in wireless communications, remote sensing, and electronic
surveillance, etc. To address the high-speed sampling bottleneck of wideband
spectrum sensing, a fast and practical solution of power spectrum estimation
for Nyquist folding receiver (NYFR) is proposed in this paper. The NYFR
architectures is can theoretically achieve the full-band signal sensing with a
hundred percent of probability of intercept. But the existing algorithm is
difficult to realize in real-time due to its high complexity and complicated
calculations. By exploring the sub-sampling principle inherent in NYFR, a
computationally efficient method is introduced with compressive covariance
sensing. That can be efficient implemented via only the non-uniform fast
Fourier transform, fast Fourier transform, and some simple multiplication
operations. Meanwhile, the state-of-the-art power spectrum reconstruction model
for NYFR of time-domain and frequency-domain is constructed in this paper as a
comparison. Furthermore, the computational complexity of the proposed method
scales linearly with the Nyquist-rate sampled number of samples and the
sparsity of spectrum occupancy. Simulation results and discussion demonstrate
that the low complexity in sampling and computation is a more practical
solution to meet the real-time wideband spectrum sensing applications
Multiband Spectrum Access: Great Promises for Future Cognitive Radio Networks
Cognitive radio has been widely considered as one of the prominent solutions
to tackle the spectrum scarcity. While the majority of existing research has
focused on single-band cognitive radio, multiband cognitive radio represents
great promises towards implementing efficient cognitive networks compared to
single-based networks. Multiband cognitive radio networks (MB-CRNs) are
expected to significantly enhance the network's throughput and provide better
channel maintenance by reducing handoff frequency. Nevertheless, the wideband
front-end and the multiband spectrum access impose a number of challenges yet
to overcome. This paper provides an in-depth analysis on the recent
advancements in multiband spectrum sensing techniques, their limitations, and
possible future directions to improve them. We study cooperative communications
for MB-CRNs to tackle a fundamental limit on diversity and sampling. We also
investigate several limits and tradeoffs of various design parameters for
MB-CRNs. In addition, we explore the key MB-CRNs performance metrics that
differ from the conventional metrics used for single-band based networks.Comment: 22 pages, 13 figures; published in the Proceedings of the IEEE
Journal, Special Issue on Future Radio Spectrum Access, March 201
- âŠ