Network service recovery from multiple correlated failures is a major concern given the increased level of infrastructure vulnerability to natural disasters, massive power failures, and malicious attacks. To properly address this problem, a novel path protection solution is proposed to jointly incorporate traffic engineering and risk minimization objectives. The framework assumes probabilistic link failures and is evaluated against some existing multi-failure recovery schemes using network simulation.

In this work, a cognitive relay network (CRN) with interference constraint from the primary user (PU) with a mobile end user is studied. The proposed system model employs a half-duplex transmission between a single PU and a single secondary user (SU). In addition, an amplify and forward (AF) relaying technique is employed between the SU source and SU destination. In this context, the mobile end user (SU destination) is assumed to move at high vehicular speeds, whereas other nodes (SU Source, SU relays and PU) are assumed to be stationary. The proposed scheme dynamically determines the best relay for transmission based on the highest signal-to-noise (SNR) ratio by deploying selection combiner at the SU destination, thereby achieving diversity. All channels connected with the stationary nodes are modelled using Rayleigh distribution, whereas all other links connected with the mobile end user are modelled using Nakagami-m fading distribution ($m<1$). The outage probabilities (OPs) are obtained considering several scenarios and Monte Carlo simulation is used to verify the numerical results. The obtained results show that a variety of factors, including the number of SU relays, the severity of the fading channels, the position of the PU, the fading model, and the mobile end user speed, might influence the CRN’s performance.

Wireless sensor network (WSN) has become a very popular technology with a wide range of applications. It consists of several spatially distributed sensors that work collaboratively to monitor a given region of interest (ROI). The limited energy available for each sensor node is a crucial restriction that affects the overall performance of the network. Therefore, energy efficiency is a major concern in WSNs. Over the years, many techniques have been developed and used to reduce energy consumption in WSNs. Clustering is one of the most effective energy-saving techniques that significantly can improve the efficiency of WSNs in terms of the network lifetime, energy consumption, and the number of received packets. In this paper, an energy-efficient algorithm for cluster head (CH) selection based on a newly formulated fitness function and using the manta ray foraging optimization (MRFO) is proposed. The objective function for the proposed formulation takes into account different network parameters such as the average distance between the CH and the sensors in its cluster, the distance between CHs and the base station (BS), residual energy, and CH balancing. The proposed algorithm is tested by running many simulations under a variety of conditions. The simulation results showed that the proposed algorithm has a better performance than that of some other algorithms reported in the literature in terms of energy consumption, networks lifetime, and the number of received packets.

Cognitive relay network is a spectrum dynamic paradigm that exploits the unused portions of the licensed spectrum. This is based on merging both cooperative relaying techniques and cognitive radio network to achieve spectrum efficiency and enhance the overall system performance. In this paper, the presence of mobile users at the destination node is considered. Here, the end users can navigate at relatively fast vehicular velocities causing dynamic multipath fading and high Doppler shift. which can be fairly modelled using Nakagami‐fading channel (i.e. ). In a spectrum scarcity environment, a secondary user must deploy an optimal power allocation policy to get higher transmission rates while the overall interference affecting the primary user (PU) is kept below a certain threshold value. In particular, the outage probability (OP) performance is studied over the mixed Rayleigh and Nakagami‐ m fading channels for different scenarios and a tight closed‐form expressions are derived for the system OP of underlay dual‐hop cognitive relay networks with a single amplifiy‐and‐forward (AF) relay with and without the use of the direct link transmission and selection diversity at the destination with interference power constraints for the primary network over independent and non‐identical (i.n.i.d) Rayleigh and Nakagami‐ m fading channels when based on the statistical characteristics of signal‐to‐noise ratio. Numerical results are presented to evaluate the impact of the fading parameter, m , the maximum aggregated intrusion constraint, and the locations of the primary users (PUs) on different channel scenarios at high vehicular speeds. Monte Carlo simulations are presented to verify and validate analytical results.

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.