Unmanned aerial vehicles (UAV), also known as drones, is an aircraft without a human pilot onboard and controlled simultaneously by computers remotely. UAVs have been employed in different applications which include fire fighting, security, data coverage, and information transformation. UAVs represent a key technology for next-generation wireless networks that support internet of things (IoT) systems and smart cities. However, one of the bottlenecks of UAV communications systems is power consumption. Most of the UAV energy is consumed on the propulsion part which affects the ability of the UAV to transfer information. Energy harvesting can be incorporated into UAV systems to reduce network operating costs and carbon footprints. Hence, we formulate a trajectory optimization problem for UAV-based communications systems powered by energy harvesting. Then, we provide a solution based on convex optimization to tackle the UAV energy efficiency constraint. Indeed, this paper presents an energy-efficient scheme based on simultaneously powering a UAV with solar energy and optimizing the trajectory to increase the energy efficiency of the drone. This approach has not only been shown to increase the energy efficiency of the drone but also decrease the carbon footprints. Numerical simulations are done to show the efficiency of the proposed scheme.

Remote monitoring systems analyze the environment dynamics in different smart industrial applications, such as occupational health and safety, and environmental monitoring. Specifically, in Industrial Internet of Things (IIoT) systems, the huge number of devices and the expected performance put pressure on resources, such as computational, network, and device energy. Distributed training of machine and deep learning (ML/DL) models for intelligent industrial IoT applications is very challenging for resource limited devices over heterogeneous wireless networks (HetNets). Hierarchical federated learning (HFL) performs training at multiple layers offloading the tasks to nearby multiaccess edge computing (MEC) units. In this article, we propose a novel energy-efficient HFL framework enabled by wireless energy transfer (WET) and designed for heterogeneous networks with massive multiple-input–multiple-output (MIMO) wireless backhaul. Our energy-efficiency approach is formulated as a mixed-integer nonlinear programming (MINLP) problem, where we optimize the HFL device association and manage the wireless transmitted energy. However due to its high complexity, we design a heuristic resource management algorithm, namely, H2RMA, that respects energy, channel quality, and accuracy constraints, while presenting a low-computational complexity. We also improve the energy consumption of the network using an efficient device scheduling scheme. Finally, we investigate device mobility and its impact on the HFL performance. Our extensive experiments confirm the high performance of the proposed resource management approach in HFL over HetNets, in terms of training loss and grid energy costs.

In this paper, we investigate an energy-efficient Long Range (LoRa) wireless network powered by hybrid energy which consists of an energy harvesting source and the grid. The grid allows to compensate for the randomness and intermittency of the harvested energy. The aim is to propose a dynamic energy-efficient resource management scheme for LoRa wireless networks that enables green Internet of Things (IoT). Hence, we formulate a grid energy cost minimization problem subject to minimum received signal-to-noise ratio (SNR), and channel, spreading factor (SF) and energy availability constraints. The formulated problem is simplified and decoupled into two sub-problems which allows to derive the optimal resource management solution but with high computational complexity. Then, we propose a low complexity heuristic channel and SF assignment, and energy management algorithm for dynamic LoRa wireless networks. Numerical results shows the efficient use of renewable energy in green dynamic LoRa wireless networks.

LoRa wireless networks are considered as a key enabling technology for next-generation Internet of Things (IoT) systems. New IoT deployments (e.g., smart city scenarios) can have thousands of devices per square kilometer leading to huge amount of power consumption to provide connectivity. In this article, we investigate green LoRa wireless networks powered by a hybrid of the grid and renewable energy sources, which can benefit from harvested energy while dealing with the intermittent supply. This article proposes resource management schemes of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway energy efficiency. First, the problem of grid power consumption minimization while satisfying the system’s quality of service demands is formulated. Specifically, both scenarios the uncorrelated and time-correlated channels are investigated. The optimal resource management problem is solved by decoupling the formulated problem into two subproblems: 1) channel and SF assignment problem and 2) energy management problem. Since the optimal solution is obtained with high complexity, online resource management heuristic algorithms that minimize the grid energy consumption are proposed. Finally, taking into account the channel and energy correlation, adaptable resource management schemes based on reinforcement learning (RL) are developed. Simulation results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

In this article, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base stations (BSs) employs massive multiple-input–multiple-output (MIMO) to serve a set of users powered by independent energy harvesting sources. Since a certain number of users may not be able to participate in FL due to interference and energy constraints, a joint energy management and user scheduling problem in FL over wireless systems is formulated. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To find how the transmit power, the number of scheduled users and user association, affect the training loss, the FL convergence rate is first analyzed. Given this analytical result, the original optimization problem can be decomposed, simplified, and solved. Simulation results show that the proposed user scheduling and user association algorithm can reduce training loss compared to a standard FL algorithm.

LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

In this paper, the deployment of federated learning (FL) is investigated in an energy harvesting wireless network in which the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) system and a set of users powered by independent energy harvesting sources to cooperatively perform FL. Since a certain number of users may not be served due to interference and energy constraints, a joint energy management and user scheduling problem is considered. This problem is formulated as an optimization problem whose goal is to minimize the FL training loss via optimizing user scheduling. To determine the effect of various wireless factors (transmit power and number of scheduled users) on training loss, the convergence rate of the FL algorithm is analyzed. Given this analytical result, the original user scheduling and energy management optimization problem can be decomposed, simplified and solved. Simulation results show that the proposed algorithm can reduce training loss compared to a standard FL algorithm.

Training centralized machine learning (ML) models becomes infeasible in wireless networks due to the increasing number of internet of things (IoT) and mobile devices and the prevalence of the learning algorithms to adapt tasks in dynamic situations with heterogeneous networks (HetNets) and battery limited devices. Hierarchical federated learning (HFL) has been proposed as a promising learning that can preserve the data privacy of the wireless devices, tackle the communication bottlenecks in wireless networks, and improve the energy effi-ciency. We propose a novel energy-efficient HFL framework for HetNets with massive multiple-input multiple-output (MIMO) wireless backhaul enabled by wireless energy transfer (WET). We formulate a joint energy management and device association optimization problem in HFL over HetNets subject to maximal divergence constraints. Next, an optimal solution is developed, but with high complexity. To reduce the complexity, a heuristic algorithm for HFL over HetNets with energy, channel quality, and accuracy constraints, is developed in order to minimize the grid energy consumption cost and preserve the value of loss function, which captures the HFL performance. Simulation results show the efficiency of the proposed resource management approach in the HFL context in terms of grid power consumption cost and training loss.

Long Range (LoRa) wireless networks, which is made up of low-powered connected devices, is a key technology for next generation wireless networks that support internet of things applications. Specifically, LoRa devices (LDs) may be powered by energy harvesting sources for greening wireless communications systems. Furthermore, LoRa modulation is based on the chirp spreading modulation (CSM) which consists of assigning various orthogonal spreading factors (SFs) among the LDs in the network. Hence, this paper investigates energy-efficient resource allocation in LoRa wireless networks, where the LDs are powered by independent energy harvesting sources. First, the problem of maximizing the number of scheduled LDs under quality of service constraints and making use of the available harvested energy, is formulated. Next, the relationship between the assigned SF, the instantaneous channel coefficients and the available energy at the batteries is analytically established. Hence, the optimal energy management, device scheduling, and SF assignment algorithm is proposed. Simulations results shows that the proposed resource allocation approaches offer efficient use of renewable energy which allows to enhance the rate of successful transmissions.

The chirp spreading modulation (CSM) is used as modulation technique for Long Range (LoRa) wireless networks that support internet of things (IoT) systems. However, this transmission scheme is limited in terms of spectral efficiency. Hence, a novel index modulation technique is proposed for CSM wireless communications systems to enhance the spectral efficiency. The proposed scheme is based on using a variety of spreading factors (SFs) in CSM systems in order to convey additional bits. The performance analysis of the proposed scheme is analytically investigated by deriving the symbol error rate. Moreover, the system performance of the proposed scheme may be investigated in terms of symbol error rate (SER) via numerical simulations and its superiority compared to the conventional CSM transmission scheme is shown.

It is vital and challenging to devise new efficient transmission techniques for next generation wireless networks that support internet of things (IoT) systems. Long range (LoRa) wireless networks are based on the deployment of connected devices with limited energy and where end-devices need higher data rates. This system is a key technology that enables smart city applications. The chirp spread spectrum is used as the modulation technique for LoRa networks which consists of assigning various orthogonal spreading factors (SF) among the connected devices in the network. In particular, each device uses a fixed SF for data transmission, which is assigned based on its distance from the gateway. This paper proposes a new SF assignment scheme aiming at enhancing the overall performance. In particular, the proposed scheme no longer assigns SFs based on the distance; but instead, it assigns them depending on the instantaneous channel realizations. Such a dynamic assignment of the SFs among LoRa users significantly enhances the overall performance compared to conventional SF assignment schemes. The proposed system is evaluated in terms of symbol error rate (SER) via numerical simulations.

In this paper, we investigate a cell-free massive MIMO system that is compromised of a large number of distributed access points (APs) powered by independent micro-grids, each with different prices. We enable this system with energy exchange capabilities in order to offset the power consumption cost. Moreover, we exploit the cooperation between the APs through energy exchange via a smart grid infrastructure in order to enhance the energy efficiency of massive MIMO systems. Hence, the problem of total grid power consumption minimization has to be solved by efficiently managing the power delivered from different sources while satisfying the system requirements in terms of user quality of service demands. This paper solves the optimal power cooperation and allocation problem using linear programming. In addition, the optimal power allocation problem is solved when neglecting cooperation between APs. Next, a joint AP selection and user scheduling algorithm is devised ensuring the feasibility of the problem. Finally, simulation results show that the proposed power cooperation technique allows to significantly enhance the energy efficiency of cell-free massive MIMO systems.

We investigate in this paper the energy efficiency of cell-free massive MIMO systems made up of a set of distributed access points, each of which is powered by both an independent energy harvesting source and the grid. The grid energy source allows to compensate for the randomness and intermittency of the harvested energy. Moreover, we enable this system with energy exchange capabilities through a smart-grid infrastructure in order to enhance the energy efficiency of massive MIMO systems. Indeed, the problem of minimizing the grid power consumption has to be solved by efficiently managing the energy delivered from different sources while satisfying the system requirements in terms of users' quality of service demands. First, the optimal offline energy cooperation and management problem is solved using linear programming. Next, we investigate the online energy cooperation and management problem by proposing an efficient online algorithm based on energy prediction. Simulations results shows that the proposed energy cooperation and management approaches offer efficient use of non-renewable energy to compensate the variability of renewable energy in cell-free MIMO systems.

This paper proposes a new transmission technique for heterogeneous networks with massive MIMO wireless back-haul with the objective of minimizing the power consumption cost. We assume that transmissions occur during two phases. During the first phase, the multi-antenna small-cell base stations (SBSs) receive signals from their associated users and from the macro-cell base station (MBS) thanks to MIMO spatial multiplexing. In the second phase, the SBSs transmit signals to the MBS and to the users. We study the problem of minimizing the sum SBS transmit power under minimum-rate constraints required at the users. We solve the formulated problem by deriving analytically the optimal time splitting parameter and the allocated transmit powers. Compared with the well-known reverse time division duplex (RTDD) with bandwidth splitting, considered as a benchmark, simulations show that the proposed transmission technique allows the SBSs to reduce considerably the power consumption.

This paper investigates the downlink of an energy efficient distributed large-scale MIMO system. The studied system is assumed to be made up of a set of remote radio heads (RRHs), each of which is powered by both an independent energy harvesting source and the grid. The grid energy allows to compensate for the randomness and intermittency of the harvested energy. Hence, the problem of grid power consumption minimization under quality of service (QoS) constraints has to be solved. First, this paper solves the optimal offline version of the problem using linear programming. Next, an iterative link removal algorithm is proposed in order to ensure the feasibility of the problem. Finally, the optimal online energy management algorithm is also proposed to solve the same problem. Simulation results show the performance of the proposed algorithms. The proposed approach in this paper allows efficient use of non-renewable energy to compensate the variability of renewable energy in large-scale MIMO systems.

In this paper, we investigate the downlink of large-scale MIMO systems considering two practical constraints related to system power. More precisely, we consider a non-negligible circuit power consumption and we impose a per-antenna power constraint due to limitations on the linearity of the RF power amplifier. Hence, a sum-rate maximization problem considering the two constraints is formulated for conjugate beamforming and zero forcing beamforming. Next, we propose efficient greedy antenna selection and power allocation algorithms in order to heuristically solve the formulated problem with reasonable computational complexity. Simulation results show the efficiency of the proposed algorithms compared to random antenna selection and optimal brute force antenna selection.

This paper investigates the downlink of massive multiple-input multiple-output (MIMO) systems that include a single cell Base Station (BS) equipped with large number of antennas serving multiple users. As the number of RF chains is getting large, the system model considered in this paper assumes a non negligible circuit power consumption. Hence, the aim of this work is to find the optimal balance between the power consumed by the RF chains and the transmitted power. First, assuming an equal power allocation among users, the optimal number of RF chains to be activated is analytically found. Then, for a given number of RF chains we derive analytically the optimal power allocation among users. Based on these analysis, we propose an iterative algorithm that computes jointly the optimal number of RF chains and the optimal power allocation vector. Simulations validate the analytical results and show the high performance provided by the proposed algorithm.

The downlink of a single cell base station (BS) equipped with large-scale multiple-input multiple-output (MIMO) system is investigated in this paper. As the number of antennas at the base station becomes large, the power consumed at the RF chains cannot be anymore neglected. So, a circuit power consumption model is introduced in this work. It involves that the maximal sum-rate is not obtained when activating all the available RF chains. Hence, the aim of this work is to find the optimal number of activated RF chains that maximizes the sum-rate. Computing the optimal number of activated RF chains must be accompanied by an adequate antenna selection strategy. First, we derive analytically the optimal number of RF chains to be activated so that the average sum-rate is maximized under received equal power. Then, we propose an efficient greedy algorithm to select the sub-optimal set of RF chains to be activated with regards to the system sum-rate. It allows finding the balance between the power consumed at the RF chains and the transmitted power. The performance of the proposed algorithm is compared with the optimal performance given by brute force search (BFS) antenna selection. Simulations allow to compare the performance given by greedy, optimal and random antenna selection algorithms.

In this paper, we implement and analyze a resource allocation protocol for distributed dynamic spectrum allocation (DSA) systems. The DSA protocol is a learning-based protocol that allows secondary users (SU) to exploit the spectrum bands efficiently in a distributed manner without the need of information exchange. The implementation and test of the proposed protocol is done using ns3 assuming that the SUs selecting the same band share it in accordance with a carrier sense multiple access (CSMA) scheme. The evaluation of the proposed protocol is done under various traffic models. We show the importance of the objective function's choice; used as a utility to be maximized in the learning. We also show the impact of various practical aspects taken into consideration while implementing the protocol on the system's achieved performance.

In this paper, we derive a new geometrical blind corner scattering model for vehicle-to- infrastructure (V2I) communications. The proposed model takes into account single-bounce and double- bounce scattering stemming from fixed scatterers located on both sides of the curved street. Starting from the geometrical blind corner model, the exact expression of the angle of departure (AOD) is derived. Based on this expression, the probability density function (PDF) of the AOD and the Doppler power spectrum are determined. Analytical expressions for the channel gain and the temporal autocorrelation function (ACF) are provided under non-line-of-sight (NLOS) conditions. Moreover, we investigate the impact of the position of transmitting vehicle relatively to the receiving road-side unit on the channel statistics. The proposed channel model is useful for the design and analysis of future V2I communication systems.