We investigate analytic solutions for three generalized integrals involving arbitrary powers, generalized Marcum Q-function and exponential functions, which are more general than the existing integrals and have been extensively employed in the performance evaluations in various areas of wireless communications, especially in cognitive radios. Closed-form solutions for the general integrals in terms of Meijer's G-function are derived and validated by comparing with the analytic expressions for the existing integrals involving the generalized Marcum Q-function, which are special cases of the general integrals addressed in this letter. Moreover, one of the analytic solutions for the general integrals is employed to derive the exact closed-form expression for the average detection probability of energy detection over the α-µ fading channels.
In this study, a novel and exact closed-form expression for detection probability of energy detection (ED) in terms of Meijer’s G-function over α-μ generalized fading channels was derived. It is more accurate and practical than the existing exact expressions and has wide application prospects in the performance evaluations in various areas of wireless communications, especially in the wireless sensor network (WSN) and the cognitive radio network (CRN). Furthermore, an exact and simple analytical solution for the sample size meeting the desired detection performance in terms of the probability mass function of a Poisson distribution was also solved. Simulations verified the detection performance and accuracy of our derived expressions with a small sample size compared to the existing exact expressions and approximations.
Spectrum handoff is one of the key techniques in a cognitive radio system. In order to improve the agility and the reliability of spectrum handoffs as well as the system throughput in hybrid cognitive radio networks (HCRNs) combing interweave mode with underlay mode, a predictive (or proactive) spectrum handoff scheme based on a deep Q-network (DQN) for HCRNs is proposed in this paper. In the proposed spectrum handoff approach, spectrum handoff success rate is introduced into an optimal spectrum resource allocation model to ensure the reliability of spectrum handoff, and the closed-form expression for the spectrum handoff success rate is obtained based on the Poisson distribution. Furthermore, we exploit the transfer learning strategy to further improve the DQN learning process and finally achieve a priority sequence of target available channels for spectrum handoffs, which can maximize the overall HCRNs throughput while satisfying constraints on secondary users’ interference with primary user, limits on the spectrum handoff success rate, and the secondary users’ performance requirements. Simulation results show that the proposed spectrum handoff scheme outperforms the state-of-the-art spectrum handoff algorithms based on predictive decision in terms of the convergence rate, the handoff success rate and the system throughput.
Spectrum sensing plays an important role in cognitive radio (CR) system which is able to detect the primary users (PU) and avoid secondary users' (SU) interfering with them. However, the traditional sensing schemes such as energy detection, cyclostationary feature detection and matched detection need know the prior knowledge which is practically hard to obtain. Meanwhile, considering the influence of hidden terminals and multipath fading problems on the sensing result, this paper introduces a novel cooperative spectrum sensing algorithm which uses the eigenvalues of sample covariance matrix based on random matrix theory to sense spectrum holes. The proposed scheme does not require any information of the transmitted signal and the noise power. Numerical results show that the proposed scheme can gain higher sensing performance with a few of secondary users and is more robust to the noise uncertainty compared with the conventional sensing schemes.
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