A Bayesian approach for adaptive multiantenna sensing in cognitive radio networks

Much of the recent work on multiantenna spectrum sensing in cognitive radio (CR) networks has been based on generalized likelihood ratio test (GLRT) detectors, which lack the ability to learn from past decisions and to adapt to the continuously changing environment. To overcome this limitation, in this paper we propose a Bayesian detector capable of learning in an efficient way the posterior distributions under both hypotheses. These posteriors summarize, in a compact way, all information seen so far by the cognitive secondary user. Our Bayesian model places priors directly on the spatial covariance matrices under both hypothesis, as well as on the probability of channel occupancy. Specifically, we use inverse-gamma and complex inverse-Wishart distributions as conjugate priors for the null and alternative hypothesis, respectively; and a binomial distribution as the prior for channel occupancy. At each sensing period, Bayesian inference is applied and the posterior for the channel occupancy is thresholded for detection. After a suitable approximation, the posteriors are employed as priors for the next sensing frame, which forms the basis of the proposed Bayesian learning procedure. We also include a forgetting mechanism that allows to operate satisfactorily on time-varying scenarios. The performance of the Bayesian detector is evaluated by simulations and also by means of CR testbed composed of universal radio peripheral (USRP) nodes. Both the simulations and our experimental measurements show that the Bayesian detector outperforms the GLRT in a variety of scenarios.The research leading to these results has received funding from the Spanish Government (MIC INN) under Projects TEC2010-19545-C04-03 (COSIMA) and CONSOLIDER-INGENIO 2010 CSD2008-00010 (COMONSENS). It also has been supported by FPI Grant BES-2011-047647

Experimental evaluation of a cooperative kernel-based approach for robust spectrum sensing

The spectrum sensing accuracy has been improved by the introduction of cooperative spectrum sensing (CSS) strategies where the spatial diversity is exploited among nonlegacy users. However, these CSS strategies also bring new impairments, such as the interference from other sources that severely degrade the sensing performance. In this paper, we evaluate experimentally our recent proposal for CSS based on kernel canonical correlation analysis (KCCA), where the effect of an interferer is also modeled. The experiments are conducted on a cognitive radio platform composed of several Universal Radio Peripheral (USRP) nodes, and the measurements show that our scheme is able of implicitly learning the surrounding environment by only exploiting the non-linear correlation among the receiver signals of each SU. Eventually, we provide comparative results where a considerable gain over a conventional energy detector is obtained in spite of the impairments provoked by external interferers.The research leading to these results has received funding from the Spanish Government (MICINN) under projects TEC2010-19545-C04-03 (COSIMA) and CONSOLIDERINGENIO 2010 CSD2008-00010 (COMONSENS)

Bayesian approach for the spectrum sensing mimo-cognitive radio network with presence of the uncertainty

A cognitive radio technique has the ability to learn. This system not only can observe the surrounding environment, adapt to environmental conditions, but also efficiently use the radio spectrum. This technique allows the secondary users (SUs) to employ the primary users (PUs) spectrum during the band is not being utilized by the user. Cognitive radio has three main steps: sensing of the spectrum, deciding and acting. In the spectrum sensing technique, the channel occupancy is determined with a spectrum sensing approach to detect unused spectrum. In the decision process, sensing results are evaluated and the decision process is then obtained based on these results. In the final process which is called the acting process, the scholar determines how to adjust the parameters of transmission to achieve great performance for the cognitive radio network

Impact of Noise Power Uncertainty on the Performance of Wideband Spectrum Segmentation

The objective of this work is to investigate the impact of noise uncertainty on the performance of a wideband spectrum segmentation technique. We define metrics to quantify the degradation due to noise uncertainty and evaluate the performance using simulations. Our simulation results show that the noise uncertainty has detrimental effects especially for low SNR users

A statistical approach to spectrum sensing using bayes factor and p-Values

The sensing methods with multiple receive antennas  in the Cognitive Radio (CR) device, provide a promising solution for reducing the error rates in the detection of the Primary User (PU) signal. The received Signal to Noise Ratio at the CR receiver is enhanced using the diversity combiners. This paper proposes a statistical approach based on minimum Bayes factors and p-Values as diversity combiners in the spectrum sensing scenario. The effect of these statistical measures in sensing the spectrum in a CR environment is investigated. Through extensive Monte Carlo simulations it is shown that this novel statistical approach based on Bayes factors provides a promising solution to combine the test statistics from multiple receiver antennas and can be used as an alternative to the conventional hypothesis testing methods for spectrum sensing. The Bayesian results provide more accurate results when measuring the strength of the evidence against the hypothesis

Analysis and Design of Multiple-Antenna Cognitive Radios with Multiple Primary User Signals

We consider multiple-antenna signal detection of primary user transmission signals by a secondary user receiver in cognitive radio networks. The optimal detector is analyzed for the scenario where the number of primary user signals is no less than the number of receive antennas at the secondary user. We first derive exact expressions for the moments of the generalized likelihood ratio test (GLRT) statistic, yielding approximations for the false alarm and detection probabilities. We then show that the normalized GLRT statistic converges in distribution to a Gaussian random variable when the number of antennas and observations grow large at the same rate. Further, using results from large random matrix theory, we derive expressions to compute the detection probability without explicit knowledge of the channel, and then particularize these expressions for two scenarios of practical interest: 1) a single primary user sending spatially multiplexed signals, and 2) multiple spatially distributed primary users. Our analytical results are finally used to obtain simple design rules for the signal detection threshold.Comment: Revised version (14 pages). Change in titl