The wireless research community aspires to conceive full duplex operation by supporting concurrent transmission and reception in a single time/frequency channel for the sake of improving the attainable spectral efficiency by a factor of two as compared to the family of conventional half duplex wireless systems. The main challenge encountered in implementing FD wireless devices…
Omnet++ Simulator
Distributed Topological Convex Hull Estimation of Event Region in Wireless Sensor Networks without Location Information
In critical event (e.g., fire or gas) monitoring applications of wireless sensor networks (WSNs), convex hull of the event region is an efficient tool in handling the usual tasks like event report, routes reconstruction and human motion planning. Existing works on estimating convex hull of event region usually require location information of sensor nodes, which…
A New Pan-Sharpening Method With Deep Neural Networks
A deep neural network (DNN)-based new pansharpening method for the remote sensing image fusion problem is proposed in this letter. Research on representation learning suggests that the DNN can effectively model complex relationships between variables via the composition of several levels of nonlinearity. Inspired by this observation, a modified sparse denoising autoencoder (MSDA) algorithm is…
TDMA-based MAC Protocols for Vehicular Ad Hoc Networks: A Survey, Qualitative Analysis and Open Research Issues
Vehicular Ad-hoc NETworks (VANETs) have attracted a lot of attention in the research community in recent years due to their promising applications. VANETs help improve traffic safety and efficiency. Each vehicle can exchange information to inform other vehicles about the current status of the traffic flow or a dangerous situation such as an accident. Road…
Directivity Estimations for Short Dipole Antenna Arrays Using Radial Basis Function Neural Networks
The role of directivity is very important in the operation of an array as it gives a measure of the effectiveness of the array in pointing the radiations in a specific direction. Traditional methods used for the computation of directivity are although effective but may be time consuming. Artificial neural networks (ANNs) do not require…
A Framework for Evaluating the Best Achievable Performance by Distributed Lifetime-Efficient Routing Schemes in Wireless Sensor Networks
This paper is concerned with energy-efficient routing in wireless sensor networks. Most of the existing routing schemes assign energy-related costs to network links and obtain the shortest paths for the nodes to balance the flowing traffic within the network and increase its lifetime. However, the optimal link cost values and the maximum achievable lifetime are…
Asymptotic Deployment Gain: A Simple Approach to Characterize the SINR Distribution in General Cellular Networks
In cellular network models, the base stations are usually assumed to form a lattice or a Poisson point process (PPP). In reality, however, they are deployed neither fully regularly nor completely randomly. Accordingly, in this paper, we consider the very general class of motion-invariant models and analyze the behavior of the outage probability (the probability…
Delay Optimal Buffered Decode-and-Forward for Two-Hop Networks With Random Link Connectivity
Delay optimal control of multi-hop networks remains a challenging problem even in the simplest scenarios. In this paper, we consider delay optimal control of a two-hop half-duplex network with independent identically distributed ON-OFF fading. Both the source node and the relay node are equipped with infinite buffers and have exogenous bit arrivals. We focus on…
Optimal Approach for Reliability Assessment in Radial Distribution Networks
This paper proposes a new methodology for the evaluation of reliability in radial distribution networks through the identification of new investments in this kind of networks, in order to reduce the repair time and the failure rate, which leads to a reduction of the forced outage rate and, consequently, to an increase of reliability. The…
From Feedforward to Recurrent LSTM Neural Networks for Language Modeling
Language models have traditionally been estimated based on relative frequencies, using count statistics that can be extracted from huge amounts of text data. More recently, it has been found that neural networks are particularly powerful at estimating probability distributions over word sequences, giving substantial improvements over state-of-the-art count models. However, the performance of neural network…