This paper presents and evaluates artificial neural network (ANN) models used for macrocell path-loss prediction. Measurement data obtained by utilizing the IS-95 pilot signal from a commercial code-division multiple-access (CDMA) mobile network in rural Australia are used to train and evaluate the models. A simple neuron model and feed-forward networks with different numbers of hidden layers and neurons are evaluated regarding their training time, prediction accuracy, and generalization properties. Furthermore, different backpropagation training algorithms, such as gradient descent and Levenberg-Marquardt, are evaluated. The artificial neural network inputs are chosen to be distance to base station, parameters easily obtained from terrain path profiles, land usage, and vegetation type and density near the receiving antenna. The path-loss prediction results obtained by using the ANN models are evaluated against different versions of the semi-terrain based propagation model Recommendation ITU-R P.1546 and the Okumura-Hata model. The statistical analysis shows that a non-complex ANN model performs very well compared with traditional propagation models with regard to prediction accuracy, complexity, and prediction time. The average ANN prediction results were 1) maximum error: 22 dB; 2) mean error: 0 dB; and 3) standard deviation: 7 dB. A multilayered feed-forward network trained using the standard backpropagation algorithm was compared with a neuron model trained using the Levenberg-Marquardt algorithm. It was found that the training time decreases from 150 000 to 10 iterations, while the prediction accuracy is maintained.

In this paper, we present the performance of fixed decode-and-forward cooperative networks with relay selection over independent but not identically distributed Nakagami-m fading channels, with integer values of the fading severity parameter m. Specifically, closed-form expressions for the symbol error probability and the outage probability are derived using the statistical characteristic of the signal-to-noise ratio. We also perform Monte-Carlo simulations to verify the analytical results.

The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods.

Perceptual quality metrics are widely deployed in image and video processing systems. These metrics aim to emulate the integral mechanisms of the human visual system (HVS) to correlate well with visual perception of quality. One integral property of the HVS is, however, often neglected: visual attention (VA) [1]. The essential mechanisms associated with VA consist mainly of higher cognitive processing, deployed to reduce the complexity of scene analysis. For this purpose, a subset of the visual information is selected by shifting the focus of attention across the visual scene to the most relevant objects. By neglecting VA, perceptual quality models inherently assume that all objects draw the attention of the viewer to the same degree.

Click on the title to browse this book

The exact closed-form expression for the outage probability (OP) of cognitive radio dual-hop amplify-and-forward (AF) relay networks is derived. The tractable expression of the OP, given in the form of elementary functions, readily enables evaluating the effect of primary users on the secondary system performance. It has been shown that the use of AF relaying significantly improves the performance of cognitive radio networks compared to its direct transmission counterpart.