A Survey of Cognitive Radio Access to TV White Spaces

Cognitive radio is being intensively researched as the enabling technology for license-exempt access to the so-called TV White Spaces (TVWS), large portions of spectrum in the UHF/VHF bands which become available on a geographical basis after digital switchover. Both in the US, and more recently, in the UK the regulators have given conditional endorsement to this new mode of access. This paper reviews the state-of-the-art in technology, regulation, and standardisation of cognitive access to TVWS. It examines the spectrum opportunity and commercial use cases associated with this form of secondary access

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

Performance of a TV white space database with different terrain resolutions and propagation models

Cognitive Radio has now become a realistic option for the solution of the spectrum scarcity problem in wireless communication. TV channels (the primary user) can be protected from secondary-user interference by accurate prediction of TV White Spaces (TVWS) by using appropriate propagation modelling. In this paper we address two related aspects of channel occupancy prediction for cognitive radio. Firstly we investigate the best combination of empirical propagation model and spatial resolution of terrain data for predicting TVWS by examining the performance of three propagation models (Extended-Hata, Davidson-Hata and Egli) in the TV band 470 to 790 MHz along with terrain data resolutions of 1000, 100 and 30 m, when compared with a comprehensive set of propagation measurements taken in randomly-selected locations around Hull, UK. Secondly we describe how such models can be integrated into a database-driven tool for cognitive radio channel selection within the TVWS environment

Design of Cognitive Radio Database using Terrain Maps and Validated Propagation Models

Cognitive Radio (CR) encompasses a number of technologies which enable adaptive self-programing of systems at different levels to provide more effective use of the increasingly congested radio spectrum. CRs have potential to use spectrum allocated to TV services, which is not used by the primary user (TV), without causing disruptive interference to licensed users by using appropriate propagation modelling in TV White Spaces (TVWS). In this paper we address two related aspects of channel occupancy prediction for cognitive radio. Firstly, we continue to investigate the best propagation model among three propagation models (Extended-Hata, Davidson-Hata and Egli) for use in the TV band, whilst also finding the optimum terrain data resolution to use (1000, 100 or 30 m). We compare modelled results with measurements taken in randomly-selected locations around Hull UK, using the two comparison criteria of implementation time and accuracy, when used for predicting TVWS system performance. Secondly, we describe how such models can be integrated into a database-driven tool for CR channel selection within the TVWS environment by creating a flexible simulation system for creating a TVWS database

Sensing Throughput Tradeoff for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithm, and examines the sensing throughput tradeoff for cognitive radio (CR) networks under noise variance uncertainty. It is assumed that there are one white sub-band, and one target sub-band which is either white or non-white. Under this assumption, first we propose a novel generalized energy detector (GED) for examining the target sub-band by exploiting the noise information of the white sub-band, then, we study the tradeoff between the sensing time and achievable throughput of the CR network. To study this tradeoff, we consider the sensing time optimization for maximizing the throughput of the CR network while appropriately protecting the primary network. The sensing time is optimized by utilizing the derived detection and false alarm probabilities of the GED. The proposed GED does not suffer from signal to noise ratio (SNR) wall (i.e., robust against noise variance uncertainty) and outperforms the existing signal detectors. Moreover, the relationship between the proposed GED and conventional energy detector (CED) is quantified analytically. We show that the optimal sensing times with perfect and imperfect noise variances are not the same. In particular, when the frame duration is 2s, and SNR is -20dB, and each of the bandwidths of the white and target sub-bands is 6MHz, the optimal sensing times are 28.5ms and 50.6ms with perfect and imperfect noise variances, respectively.Comment: Accepted in CROWNCOM, June 2014, Oulu, Finlan

Max-Min SNR Signal Energy based Spectrum Sensing Algorithms for Cognitive Radio Networks with Noise Variance Uncertainty

This paper proposes novel spectrum sensing algorithms for cognitive radio networks. By assuming known transmitter pulse shaping filter, synchronous and asynchronous receiver scenarios have been considered. For each of these scenarios, the proposed algorithm is explained as follows: First, by introducing a combiner vector, an over-sampled signal of total duration equal to the symbol period is combined linearly. Second, for this combined signal, the Signal-to-Noise ratio (SNR) maximization and minimization problems are formulated as Rayleigh quotient optimization problems. Third, by using the solutions of these problems, the ratio of the signal energy corresponding to the maximum and minimum SNRs are proposed as a test statistics. For this test statistics, analytical probability of false alarm ($P_f$) and detection ($P_d$) expressions are derived for additive white Gaussian noise (AWGN) channel. The proposed algorithms are robust against noise variance uncertainty. The generalization of the proposed algorithms for unknown transmitter pulse shaping filter has also been discussed. Simulation results demonstrate that the proposed algorithms achieve better $P_d$ than that of the Eigenvalue decomposition and energy detection algorithms in AWGN and Rayleigh fading channels with noise variance uncertainty. The proposed algorithms also guarantee the desired $P_f(P_d)$ in the presence of adjacent channel interference signals

Dynamic spectrum access based on cognitive radio within cellular networks

Overlay transmissions in cognitive radio (CR) permit a secondary system to use spectrum concomitantly with a primary system, though adopting this spectrum sharing strategy presents a number of challenges, such as the requirement for a secondary user to have a priori knowledge as side information about the primary user. In this paper, a cognitive cellular network is proposed which uses an overlay approach to dynamically share its radio resource by incorporating cognition, leading to enhanced cell capacity. To compensate for the interference caused by the overlay, cognitive base stations use robust dirty-paper coding in combination with variable transmission powers, which are set depending upon the location of the mobile stations. A detailed performance analysis is presented to corroborate the improved spectrum utilization achieved using this technique

From Sensing to Predictions and Database Technique: A Review of TV White Space Information Acquisition in Cognitive Radio Networks

Strategies to acquire white space information is the single most significant functionality in cognitive radio networks (CRNs) and as such, it has gone some evolution to enhance information accuracy. The evolution trends are spectrum sensing, prediction algorithm and recently, geo‐location database technique. Previously, spectrum sensing was the main technique for detecting the presence/absence of a primary user (PU) signal in a given radio frequency (RF) spectrum. However, this expectation could not materialized as a result of numerous technical challenges ranging from hardware imperfections to RF signal impairments. To convey the evolutionary trends in the development of white space information, we present a survey of the contemporary advancements in PU detection with emphasis on the practical deployment of CRNs i.e. Television white space (TVWS) networks. It is found that geo‐location database is the most reliable technique to acquire TVWS information although, it is financially driven. Finally, using financially driven database model, this study compared the data‐rate and spectral efficiency of FCC and Ofcom TV channelization. It was discovered that Ofcom TV channelization outperforms FCC TV channelization as a result of having higher spectrum bandwidth. We proposed the adoption of an allinclusive TVWS information acquisition model as the future research direction for TVWS information acquisition techniques