Sensitive White Space Detection with Spectral Covariance Sensing

This paper proposes a novel, highly effective spectrum sensing algorithm for cognitive radio and whitespace applications. The proposed spectral covariance sensing (SCS) algorithm exploits the different statistical correlations of the received signal and noise in the frequency domain. Test statistics are computed from the covariance matrix of a partial spectrogram and compared with a decision threshold to determine whether a primary signal or arbitrary type is present or not. This detector is analyzed theoretically and verified through realistic open-source simulations using actual digital television signals captured in the US. Compared to the state of the art in the literature, SCS improves sensitivity by 3 dB for the same dwell time, which is a very significant improvement for this application. Further, it is shown that SCS is highly robust to noise uncertainty, whereas many other spectrum sensors are not

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

Spectrum Sensing for Cognitive Radio Systems Through Primary User Activity Prediction

Traditional spectrum sensing techniques such as energy detection, for instance, can sense the spectrum only when the cognitive radio (CR) is is not in operation. This constraint is relaxed recently by some blind source separation techniques in which the CR can operate during spectrum sensing. The proposed method in this paper uses the fact that the primary spectrum usage is correlated across time and follows a predictable behavior. More precisely, we propose a new spectrum sensing method that can be trained over time to predict the primary user's activity and sense the spectrum even while the CR user is in operation. Performance achieved by the proposed method is compared to classical spectrum sensing methods. Simulation results provided in terms of receiver operating characteristic curves indicate that in addition to the interesting feature that the CR can transmit during spectrum sensing, the proposed method outperforms conventional spectrum sensing techniques

Cooperative Spectrum Sensing based on the Limiting Eigenvalue Ratio Distribution in Wishart Matrices

Recent advances in random matrix theory have spurred the adoption of eigenvalue-based detection techniques for cooperative spectrum sensing in cognitive radio. Most of such techniques use the ratio between the largest and the smallest eigenvalues of the received signal covariance matrix to infer the presence or absence of the primary signal. The results derived so far in this field are based on asymptotical assumptions, due to the difficulties in characterizing the exact distribution of the eigenvalues ratio. By exploiting a recent result on the limiting distribution of the smallest eigenvalue in complex Wishart matrices, in this paper we derive an expression for the limiting eigenvalue ratio distribution, which turns out to be much more accurate than the previous approximations also in the non-asymptotical region. This result is then straightforwardly applied to calculate the decision threshold as a function of a target probability of false alarm. Numerical simulations show that the proposed detection rule provides a substantial performance improvement compared to the other eigenvalue-based algorithms.Comment: 7 pages, 2 figures, submitted to IEEE Communications Letter