Error Rate Analysis of Cognitive Radio Transmissions with Imperfect Channel Sensing

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed

Spectrum sensing via restricted neyman-pearson approach in the presence of non-Gaussian noise

In this paper, spectrum sensing in cognitive radio systems is studied for non-Gaussian channels in the presence of prior distribution uncertainty. In most practical cases, some amount of prior information about signals of primary users is available to secondary users but that information is never perfect. In order to design optimal spectrum sensing algorithms in such cases, we propose to employ the restricted Neyman-Pearson (NP) approach, which maximizes the average detection probability under constraints on the worst-case detection and false-alarm probabilities. We derive a restricted NP based spectrum sensing algorithm for additive Gaussian mixture noise channels, and compare its performance against the generalized likelihood ratio test (GLRT) and the energy detector. Simulation results show that the proposed spectrum sensing algorithm provides improvements over the other approaches in terms of minimum (worst-case) and/or average detection probabilities. © 2013 IEEE

Polarity-Coincidence-Array based spectrum sensing for multiple antenna cognitive radios in the presence of non-gaussian noise

One of the main requirements of the cognitive radio (CR) systems is the ability to perform spectrum sensing in a reliable manner in challenging environments that arise due to propagation channels which undergo multipath fading and non-Gaussian noise. While most existing literature on spectrum sensing has focused on impairments introduced by additive white Gaussian noise (AWGN), this assumption fails to model the behavior of certain types of noise in practice. In this paper, the use of a non-parametric and easily implementable detection device, namely polarity-coincidence-array (PCA) detector, is proposed for the detection of weak primary signals with a cognitive radio equipped with multiple antennas. Its performance is evaluated in the presence of heavy-tailed noise. The detector performance in terms of the probabilities of detection and false alarm is derived when the communication channels between the primary user transmitter and the multiple antennas at the cognitive radio are AWGN as well as when they undergo Rayleigh fading. From the numerical results, it is observed that a significant performance enhancement is achieved by the PCA detector compared to that of the energy detector with AWGN as well as fading channels as the heaviness of the tail of the non-Gaussian noise increases