In this paper, we study Two-Way Relaying (TWR) networks well-known for its throughput merits. In particular, we study the fundamental throughput delay trade-off in TWR networks using opportunistic network coding (ONC). We characterize the optimal ONC policy that maximizes the aggregate network throughput subject to an average packet delay constraint. Towards this objective, first, we consider a pair of nodes communicating through a common relay and develop a two dimensional Markov chain model capturing the buffers' length states at the two nodes. Second, we formulate an optimization problem

This paper proposes a cooperation protocol between a secondary user (SU) and a primary user (PU) which dedicates a free frequency subband for the SU if cooperation results in energy saving. Time is slotted and users are equipped with buffers. Under the proposed protocol, the PU releases portion of its bandwidth for secondary transmission. Moreover, it assigns a portion of the time slot duration for the SU to relay primary packets and achieve a higher successful packet reception probability at the primary receiver. We assume that the PU has three states: idle, forward, and retransmission states

We consider the problem of optimizing the performance of a cooperative cognitive radio user subject to constraints on the quality-of-service (QoS) of the primary user (PU). In particular, we design the probabilistic admission control parameter of the PU packets in the secondary user (SU) relaying queue and the randomized service parameter at the SU under non-work-conserving (non-WC) and WC cooperation policies. In the non-WC policy, two constrained optimization problems are formulated; the first problem is maximizing the SU throughput while the second problem is minimizing the SU average delay

An interference alignment (IA) scheme is presented that allows multiple opportunistic transmitters (secondary users) to use the same frequency band of a pre-existing primary link without generating any interference. The primary and secondary transmit-receive pairs are equipped with multiple antennas. Under power constraints on the primary transmitter, the rate of the primary user is maximized by water-filling on the singular values of its channel matrix leaving some eigen modes unused, and hence, the secondary users can align their transmitted signals to produce a number of interference-free

A new method is proposed for the blind subspace-based identification of the coefficients of time-varying (TV) single-input multiple-output (SIMO) FIR channels. The TV channel coefficients are represented via a finite basis expansion model, i.e. linear combination of known basis functions. In contrast to earlier related works, the basis functions need not be limited to complex exponentials, and therefore do not necessitate the a priori estimation of frequency parameters. This considerably simplifies the implementation of the proposed method and provides added flexibility in applications. The

COVID-19 is a growing problem worldwide with a high mortality rate. As a result, the World Health Organization (WHO) declared it a pandemic. In order to limit the spread of the disease, a fast and accurate diagnosis is required. A reverse transcript polymerase chain reaction (RT-PCR) test is often used to detect the disease. However, since this test is time-consuming, a chest computed tomography (CT) or plain chest X-ray (CXR) is sometimes indicated. The value of automated diagnosis is that it saves time and money by minimizing human effort. Three significant contributions are made by our

As optimization algorithms have a great power to solve nonlinear, complex, and hard optimization problems, nature-inspired algorithms have been applied extensively in distinct fields in order to solve real life optimization cases. In this paper, modifications for the recently proposed Gaining-Sharing-Knowledge based algorithm (GSK) are presented for enhancing its performance. Gaining-Sharing-Knowledge algorithm is considered as a perfect example of modern nature-inspired algorithm that considered the human life behavior as a source of inspiration in order to solve optimization problems. GSK

This paper proposes a novel nature-inspired algorithm called Gaining Sharing Knowledge based Algorithm (GSK) for solving optimization problems over continuous space. The GSK algorithm mimics the process of gaining and sharing knowledge during the human life span. It is based on two vital stages, junior gaining and sharing phase and senior gaining and sharing phase. The present work mathematically models these two phases to achieve the process of optimization. In order to verify and analyze the performance of GSK, numerical experiments on a set of 30 test problems from the CEC2017 benchmark for

In this paper, we study a two-tier cellular network with cache-enabled small base stations (SBSs). In our model, a SBS has the ability to coordinate with neighboring SBSs and fetch data from their caches. We focus on the effect of SBSs' density on the network's energy efficiency. To this end, stochastic geometry theory is used to model the SBSs and users distributions, which enables us to find closed-form expressions for the network's energy efficiency as a function of the SBSs density and the cache size at each SBS. The optimal SBSs density that maximizes the energy efficiency is

This paper studies the potential of proactive resource allocation to prolong the communication sessions in networks with limited energy budgets and stringent quality-of-service (QoS) requirement, particularly a regular service guarantee. A threshold-based proactive communication policy is proposed to minimize the consumed transmission energy and maximize the network lifetime based on the different link states and the buffer state at the destination node. A closed-form expression is presented for the proactive gain in terms of the channel gain threshold, the amount of the proactively-