In this paper, we characterize optimal resource allocation for the uplink and downlink of wireless powered cellular networks (WPCNs). In particular, we investigate a time-slotted WPCN, where a hybrid access point (HAP) is in charge of energy replenishing of M cellular users (CUs), along with transmission/reception of information to/from them. Unlike prior works, which give attention to information transmission in only one direction (either uplink or downlink), our work incorporates information transmission in both directions, along with energy transfer over the downlink. Besides harvesting

Centralized coded caching and delivery is studied for a partially-connected fog radio access network (F-RAN), whereby a set of H edge nodes (ENs) (without caches), connected to a cloud server via orthogonal fronthaul links, serve K users over the wireless edge. The cloud server is assumed to hold a library of N files, each of size F bits; and each user, equipped with a cache of size MF bits, is connected to a distinct set of r ENs; or equivalently, the wireless edge from the ENs to the users is modeled as a partial interference channel. The objective is to minimize the normalized delivery time

This paper studies the Multi-Input-Multi-Output (MIMO) interference networks with arbitrary number of transmitters and receivers, where both the transmitters and receivers are equipped with caches. Our objective is to propose content placement and delivery schemes that minimize the worst case normalized delivery time (NDT). First, we design a delivery scheme for the cache-aided Single-Input-Multiple-Output (SIMO) interference networks. Then, we obtain the achievable NDT of the cache-aided MIMO interference networks by using the decomposition property. The numerical results show the superiority

Energy harvesting (EH) is a promising technology for realizing energy-efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this paper is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I, which has unlimited energy supply and Type II, which is solely powered by an RF energy

In this paper, we present a novel wideband multiple-input multiple-output Vehicle-to-Vehicle channel model. The proposed channel model is derived using the geometrical elliptical scattering approach. In order to emulate the appearance and disappearance of scatterers (vehicles, terrain, roadside units, etc.) in the environment, we associate a persistence process with each physical scatterer in the model. The parameters of the proposed model can be tuned to represent a variety of vehicular environments. We also derive the temporal and spatial correlation functions of the channel coefficients

For the solar photovoltaic (PV) system to operate efficiently, it is necessary to effectively establish an equivalent model of PV cell and extract the relevant unknown model parameters accurately. This paper introduces a new metaheuristic algorithm, i.e., gaining-sharing knowledge based algorithm (GSK) to solve the solar PV model parameter extraction problem. This algorithm simulates the process of knowledge acquisition and sharing in the human life cycle and is with strong competitiveness in solving optimization problems. It includes two significant phases. The first phase is the beginner

This paper presents the application of gaining sharing knowledge (GSK) based optimization algorithm to an uncertain solid transportation (UST) problem. The UST problem consists of supply, demand, and conveyance constraints under uncertain environment. To solve the said problem, the expected criterion model is considered so that the expected value of the objective function is minimized. 99-method generates the expected value of the assumed uncertain variables, and the transformed problem is solved. Due to the consideration of integer decision variables, GSK is modified to integer gaining

Energy efficiency (EE) is one of the main parameters to be considered in recent networks targeting green technology. Our system is based on cloud radio access networks and it consists of a macro base-station and many small remote radio heads (RRHs). We solve an optimization problem to improve the system's EE through resource allocation and power control. We also reduce the power consumption through switching the RRHs ON/OFF based on the current users' distribution. We formulate the problem as EE maximization with constraints to provide full frequency reuse between RRHs. Our solution divides

Parkinson's disease (PD), one of whose symptoms is dysphonia, is a prevalent neurodegenerative disease. The use of outdated diagnosis techniques, which yield inaccurate and unreliable results, continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field. To solve this issue, the study proposes using machine learning and deep learning models to analyze processed speech signals of patients' voice recordings. Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival Tp slots ahead, where Tp denotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted