Network planning and design

About: Network planning and design is a research topic. Over the lifetime, 12393 publications have been published within this topic receiving 229776 citations. The topic is also known as: network design.

WaveARX neural network development for system identification using a systematic design synthesis

Abstract: The WaveARX network, a new neural network architecture, is introduced. Its development was motivated by the opportunity to capitalize on recent research results that allow some shortcomings of the traditional artificial neural net (ANN) to be addressed. ANN has been shown to be a valuable tool for system identification but suffers from slow convergence and long training time due to the globalized activation function. The structure of ANN is derived from trial and error procedures, and the trained network parameters often are strongly dependent on the random selection of the initial values. There are not even guidelines on the number of neurons needed. Also, few identification techniques are available for distinguishing linear from nonlinear contributions to a system's behavior. The WaveARX integrates the multiresolution analysis concepts of the wavelet transform and the traditional AutoRegressive eXternal input model (ARX) into a three-layer feedforward network. Additional network design problems are solved as the WaveARX formalisms provide a systematic design synthesis for the network architecture, training procedure, and excellent initial values of the network parameters. The new structure also isolates and quantifies the linear and nonlinear components of the training data sets. The wavelet function is extended to multidimensional input space using the concept of a norm. The capabilities of the network are demonstrated through several examples in comparison with some widely used linear and nonlinear identification techniques. Separately, the wavelet network of the WaveARX model is shown for the example investigated to have a better performance than two other existing wavelet-based neural networks.

Residual energy aware channel assignment in cognitive radio sensor networks

Abstract: We investigate the channel assignment problem in a cluster-based multi-channel cognitive radio sensor network in this paper. Due to the inherent power and resource constraints of sensor networks, energy efficiency is the primary concern for network design. An R-coefficient is developed to estimate the predicted residual energy using sensor information (current residual energy and expected energy consumption) and channel conditions (primary user behavior). We examine three channel assignment approaches: Random pairing, Greedy channel search and Optimization-based channel assignment. The last two exploit R-coefficient to obtain a residual energy aware channel assignment solution. Simulation results show that R-coefficient-based approaches lead to better performance in terms of energy consumption and residual energy balance. Optimization-based channel assignment outperforms the other two approaches with respect to network lifetime.

In-operation network planning

Abstract: Current transport networks are statically configured and managed, because they experience a rather limited traffic dynamicity. As a result, long planning cycles are used to upgrade the network and prepare it for the next planning period. Aimed at guaranteeing that the network can support the forecast traffic and deal with failure scenarios, spare capacity is usually installed, thus increasing network expenditures. Moreover, results from network capacity planning are manually deployed in the network, which limits the network agility. In this article, we propose a control and management architecture to allow the network to be dynamically operated. Employing those dynamicity capabilities, the network can be reconfigured and reoptimized in response to traffic changes in an automatic fashion; hence, the resource overprovisioning can be minimized and overall network costs reduced.