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In this letter, we study the performance of a singleuser fluid antenna system (FAS) under arbitrary fading distributions, in which the fading channel coefficients over the ports are correlated. We adopt copula theory to model the structure of dependency between fading coefficients. Specifically, we first derive an exact closed-from expression for the outage probability in the most general case, i.e., for any arbitrary choice of fading distribution and copula. Afterwards, for an important specific case, we analyze the performance of the outage probability under correlated Nakagami-m fading channels by exploiting popular Archimedean copulas, namely, Frank, Clayton, and Gumbel. The results demonstrate that FAS outperforms the conventional single fixed-antenna system in terms of the outage probability. We also see that the spatial correlation dependency structure for the FAS is a key factor to determine its performance, which is natively captured through the choice of copula.

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Mean square error (MSE) is the most prominent criterion in training neural networks and has been employed in numerous learning problems. In this paper, we suggest a group of novel robust information theoretic backpropagation (BP) methods, as correntropy-based conjugate gradient BP (CCG-BP). CCG-BP algorithms converge faster than the common correntropy-based BP algorithms and have better performance than the common CG-BP algorithms based on MSE, especially in nonGaussian environments and in cases with impulsive noise or heavy-tailed distributions noise. In addition, a convergence analysis of this new type of method is particularly considered. Numerical results for several samples of function approximation, synthetic function estimation, and chaotic time series prediction illustrate that our new BP method is more robust than the MSE-based method in the sense of impulsive noise, especially when SNR is low.

Spectrum sensing is a significant issue in cognitive radio networks which enables estimation of the frequency spectrum and hence provides frequency reuse. In the large-scale cognitive radio networks, secondary users cannot share a common spectrum since the coverage area of primary users is limited. In this study, the authors suggest a diffusion adaptive learning algorithm based on correntropy cooperation policy, which first categorises received data of secondary users into several groups, and then learns a common spectrum inside each group. The mean-square performance of proposed algorithm is analysed and supported by simulations. Experimental results show that, in a multitask cognitive network, the proposed algorithm can achieve a better mean-square deviation learning performance both in transient and steady-state regimes in comparison with other conventional algorithms.

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In this paper, we obtain a general achievable rate region for discrete memoryless multiple‐access relay channel (DM‐MARC) with common message via partial decode and forward strategy and regular block Markov encoding/backward decoding. This general achievable rate region, as an extension of the achievability part of the relay channel via partial decode and forward strategy to the MARC, subsumes the previous known achievable rate regions for multiple‐access channel with common message and MARC. Also, by applying Fano's inequality, we derive an outer bound for DM‐MARC with common message. By extending the discrete alphabet results to the continuous alphabet, an inner bound for Gaussian MARC (GMARC) is achieved. As expected, our results for the Gaussian MARC includes the previous results for the Gaussian multiple‐access channel with and without common message and the Gaussian relay channel as special cases. Finally, by considering a specific topology for MARC, performance of GMARC with common message versus the position of the relay is investigated numerically. Also, it is shown that our achievable rate region is larger than the previous rate region of GMARC with common message with irregular encoding/successive decoding. Copyright © 2014 John Wiley & Sons, Ltd.