Wireless-powered communication (WPC) has emerged as a cutting-edge technology for establishing self-sustaining wireless networks. However, WPC systems face numerous challenges, such as short-range energy transfer (ET) limitation, variable energy availability, and the need for an intelligent energy management system. This article addresses these challenges by proposing a bidirectional energy borrowing (EB)-assisted integrated information and energy relaying (bidirectional-EBi2ER) protocol. The proposed protocol integrates a self-sustainable hybrid access point (HAP) that borrows energy from the power grid and an uncrewed aerial vehicle (UAV) as a relay for information and energy transfer (ET) in a bidirectional communication system. A nonconvex optimization problem is formulated as a reinforcement learning problem to ensure prudent EB while maximizing the net bit rate in bidirectional communication. In the real-world environment, energy harvesting (EH) patterns and channel parameters are unknown and unpredictable. Thus, deep deterministic policy gradient (DDPG) algorithm is used to obtain the channel statistics and EH patterns. Extensive simulations assess the efficacy of the bidirectional-EBi2ER protocol, showing average improvements of 36.03% and 33.89% for linear and nonlinear EH models, respectively, along with stable convergence. In addition, the analysis provides detailed insights into modulation schemes, sensitivity analysis, threshold capacities, and optimization parameters.

A radio frequency (RF) power amplifier (PA) plays an important role to amplify the message signal at higher power to transmit it to a distant receiver. Due to a typical nonlinear behavior of the PA at high power transmission, digital predistortion (DPD), exploiting the preinversion of the nonlinearity, is used to linearize the PA. However, in a massive MIMO (mMIMO) transmitter, a single DPD is not sufficient to fully linearize multiple PAs. Further, for the full linearization, assigning a separate DPD to each PA is complex and not economical. In this work, we address these challenges via the proposed low-complexity DPD (LC-DPD) scheme. Initially, we describe the fully-featured DPD (FF-DPD) scheme to linearize the multiple PAs and examine its complexity. Thereafter, using it, we derive the LC-DPD scheme that can adaptively linearize the PAs as per the requirement. The coefficients in the two schemes are learned using the algorithms that adopt indirect learning architecture based recursive prediction error method (ILA-RPEM) due to its adaptive and free from matrix inversion operations. Furthermore, for the LC-DPD structure, we have proposed three algorithms based on correlation of its common coefficients with the distinct coefficients.

A radio frequency (RF) power amplifier (PA) is crucial to enhance the signal to transmit via antenna over long distances. High-power transmission often leads to nonlinear behavior in the PA, necessitating the use of digital predistortion (DPD) signal processing to restore linearity by preinverting the nonlinearity. However, when dealing with a massive MIMO (mMIMO) transmitter with numerous PAs, a single DPD is not enough, and allocating a separate DPD for each PA is intricate and cost-inefficient. In this study, we tackle these challenges through our proposed low-complexity DPD (LC-DPD) architecture. The LC-DPD has the flexibility to choose the parameters of its architecture as per the desired tradeoff between the performance and complexity in the linearization. It employs learning of its coefficients through algorithms utilizing an indirect learning architecture based recursive prediction error method (ILA-RPEM), which is adaptive and free from matrix inversions.

Due to high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) are emerging as an alternative for reliable wireless communications. The main challenge of integrating UAVs with cellular networks is their limited on-board energy capacity, which restricts their operation period. Hence, this article examines the energy-efficiency (EE) maximization under the constraint of UAV’s propulsion and data reception energy. Specifically, the formulated problem optimizes the user associations with UAV or base station, their respective transmit power allocations, and UAV’s trajectory subject to the user data rate requirements. As this joint optimization problem is combinatorial and involves multiple variables, we have reduced it into an equivalent tractable form using the Markov decision process (MDP). Later we leverage the deep reinforcement learning (DRL) framework based on a deep deterministic policy gradient (DDPG) algorithm to learn the UAV’s trajectory. The proposed green DRL algorithm improves the total EE of the system by 15.63% compared to the benchmark particle swarm optimization.

To understand the potential threat of the energy-efficient jamming attacker in a secure orthogonal frequency division multiple access (OFDMA) based industrial internet of things (IIoT) network, in this work, we investigate the optimal jamming to IIoT users under limited power constraint. In particular, we design an optimization problem to maximize the attacker energy-efficiency (AEE) by jointly optimizing the power allocation over the subcarriers of the IIoT users. To realize its globally optimal solution, first, we formulate an equivalent optimization problem by converting the original fractional objective function into a parametric subtractive concave function. Thereafter, the optimal point is obtained using a superlinear fast converging iterative algorithm based on Dinkelbach method that exploits Karush-Kuhn-Tucker (KKT) conditions. Via numerical results, we obtain various insights on the system performance with respect to the system parameters and lastly, the jointly optimal scheme is compared with other benchmark jamming strategies to quantity the performance.

To develop a cyber-physical artificial intelligence enabled wireless network, it is essential to support unprecedented high throughput and efficient spectrum utilization in a practically unknown channel. In this regard, we need to investigate the design aspects of the network exploiting deep learning-based non-orthogonal multiple access (NOMA) for a model-free environment. In this work, a model-free deep learning algorithm based on deep deterministic policy gradient is proposed that provides a continuous course of actions under the optimal policy for an untrusted NOMA network. Utilizing the concept of physical layer security, we focus on maximizing the sum secrecy rate of the system in terms of decoding order and transmitting power allocation to users under the limited energy constraint at the base station. Via extensive simulations, while training, we measure the performance of the deep learning algorithm in terms of cumulative sum secrecy rate, convergence rate and stability. Also, after the training, we obtain various insights on the performance of the obtained optimal policy by varying the independent system parameters and compare the algorithm against a benchmark that provides the improvement of nearly 55% in the noisy channel.

Cooperative relaying in RF powered communication solves the problems related to long range energy and information transfer. However, there is a necessity of learning based algorithms for incorporating composite processes in an unknown environment, to get the optimal policy for efficient energy and information transfer. In this paper, we propose a deep learning algorithm based on deep deterministic policy gradient (DDPG), providing continuous course of actions under optimal online policy for selection based integrated information and energy relaying (Si2ER) network. The designed problem defined is a nonconvex problem where the end-to-end average net bit rate is maximized in the four phases of operations under the given constraints on the harvested energy at relay and source nodes. Via extensive simulations, more insights are obtained on the performance of the proposed algorithm in different used modulation for transmission and learning rate while and after learning. Finally, the achieved bit rate in the Si2ER network is compared with the performance of a greedy benchmark scheme and get an improvement upto 70.72%.

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where Internet of Things (IoT) devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this article has proposed an energy-efficient UAV-assisted IoT network where a low-altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively, transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt a reinforcement learning (RL)-based solution methodology that effectively decouples it into three individual optimization subproblems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive state–action–reward–state–action, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

Beamforming technology can enhance the throughput of a multi-antenna secondary user (SU) system. However, its performance depends on the accuracy of channel estimation (CE) that is influenced by CE duration. In this work, we present a three-phase transmission approach for a multi-antenna SU, where, in the first phase, the SU performs spectrum sensing, thereafter, the channel is estimated in the second phase followed by the data is transmitted using beamforming in the third phase. Consequently, based on it, we define a frame structure and closed-form expressions for SU's average throughput as well as interference energy received at the primary user's (PU's) receiver are derived. Numerical results validate the analysis and provide insights on the impact of frame duration and CE duration on SU's average throughput. Lastly, we investigate the throughput-interference tradeoff problem numerically with different values of CE duration.

To provide seamless services by low powered small devices having stringent energy constraint in Internet of Things (IoT) networks, it is highly sought over the past few decades to design a competent energy-aware network. In that sense, thereof, the strong minimum energy topology (SMET) is investigated in the existing works to minimize the total power consumption while maintaining the strong connectivity between any pair of nodes (small IoT devices) in the network consisting only bidirectional links. Nevertheless, to significantly improve it, in this paper, we further explore the SMET with respect to insertion of a new node among the existing nodes which is defined as node insertion problem (NIP) for SMET (NIP-SMET). It has been proved that the NIP-SMET is NP-complete, therefore, we propose a heuristic based on Prim-incremental power greedy heuristic to solve it in polynomial time. Also, analytically, it has been shown that the NIP-SMET can provide significant energy-aware improvement over SMET. Via obtained numerical results, we find that the proposed heuristic can reduce the total power dissipation by 50% against the existing heuristic for SMET.

Energy Borrowing (EB) aided Energy harvesting (EH) systems provide a greener alternative to self-sustaining electronic devices in a complex, unprecedented environment by borrowing energy from a supplementary source to regulate the data transmission flow. We propose a reinforcement learning-based algorithm for energy scheduling policy which jointly optimizes the EB and utilizes harvested energy for efficient data transfer at every time instant. As the exact pattern of harvested energy and channel conditions at any time slot is unknown, the proposed algorithm, BEAR (Borrowing Energy with Adaptive Rewards), based on actor-critic architecture, learns the optimal power allocation policy for the transmission node. Our designed reward function accommodates the concept of adaptive penalty to punish the transmission node for selecting unfavourable actions. Our simulations show that the BEAR algorithm providing efficient energy management with a focus on throughput maximization yields a 35.45% enhancement in sum throughput over a typical non-borrowing system. Lastly, nontrivial design insights are outlined via numerical results to quantify the practical efficacy of BEAR for EH systems.

In this paper, we have proposed an energy-efficient unmanned aerial vehicle (UAV) assisted Internet of things (IoT) network where a low altitude UAV is employed as a mobile data collector. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, device association and respective transmit power allocation at every time slot while ensuring that every device should achieve a given transmission rate constraint. As this joint optimization problem is nonconvex and combinatorial, we adopt reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization problems. The formulated problem is transformed as a Markov decision process (MDP) where UAV learns its trajectory according to its current state and corresponding action aiming to maximize the reward under the current policy. Finally, we conceive state-action-reward-state-action (SARSA), a low complexity iterative algorithm for updating the current policy in the case of randomly deployed IoT devices which achieves good computational complexity-optimality tradeoff via numerical results. We find that the proposed methodology reduces the total energy consumption of all devices by 9.23%, 14.06%, and 15.87% in the case of 80, 100, and 120 available time slots of UAV respectively.

Intelligent reflecting surface (IRS) is an emerging technology for beyond fifth-generation (B5G) networks conceived from metamaterials that enhances the communication channel through controllable passive reflecting of transmit signals. However, the IRS-assisted communication model and optimization of available resources need to be improved further from existing works. In this paper, we obtain the expression of reflection coefficient of IRS panel by exploiting the given data of radar communications. And, using the reflection coefficient, we derive the expression of received power that incorporates the free space loss, reflection loss factor, physical dimension of the IRS panel, and radiation pattern of the transmit signal. Moreover, to maximize the received power, we jointly optimize the reflective phase shift and location of the IRS panel. To obtain more pursuits, we also investigate the semi-adaptive schemes where the phase shift and location are individually optimized while keeping other at a fixed value and examine the global optimality of obtained solutions. Lastly, via obtained numerical results, we get key insights on proposed analysis and optimal solution for different schemes.

Energy-efficient resource allocation to achieve desired throughput over energy-aware power line communications (PLCs) has gained a growing interest from the past few years. In order to improve it further, we propose a joint solution methodology for efficient utilization of available resources to minimize energy-aware outage probability in an amplified-and-forward (AF) relay-assisted PLC. In this regard, first, we derive a closed-form expression for energy-efficiency based outage probability. Using the statistical properties of the outage probability, an equivalent problem, more tractable for optimizations, is formulated and respective closed-form solutions are obtained for individual and joint optimization of relay location and power allocation (PA) over the transmit modems. Using the numerical investigations, we validate our outage analysis and describe the design insights on the obtained optimal solution. Finally, it is shown that the joint optimization provides an outage improvement of around 55% against a benchmark scheme.

Due to increasing quality-of-service (QoS) demand in already congested radio spectrum, there is a need for designing energy-efficient free space optical (FSO) communication networks. Considering a realistic fading model incorporating the fluctuations in angle-of-arrival, we minimize the outage probability for error free transmission of high data volumes through optimizing the power allocation (PA) and relay placement (RP) in a dual-hop decode-and-forward (DF) relay-assisted cooperative FSO communication with coherent detection and direct link unavailability. As this problem is nonconvex, first the optimal PA between source and relay is obtained using a global optimization algorithm. Also, a closed form for the solution is obtained using a tight analytical approximation with the assumption that atmospheric turbulence over both the links is nearly same. Next, we optimize the RP followed by the outage probability is jointly minimized using alternating optimization algorithm. Numerical results validate the outage analysis and provide key insights on optimal PA and RP yielding an outage enhancement of around 37% over the benchmark scheme.

To address the ever-increasing data traffic demand, there is a need for novel cost-efficient network deployment schemes. In this work, we investigate the joint optimization of base station (BS) location, its density, and transmit power allocation to minimize the overall network operational cost required to meet an underlying coverage constraint at each user equipment (UE), which is randomly deployed following the binomial point process. As the joint optimization problem is nonconvex and combinatorial in nature, we propose a non-trivial solution methodology that effectively decouples it into three individual optimization problems. Firstly, by using the distance distribution of the farthest UE from the BS, we present novel insights on optimal BS location for a given number of BSs and sectoring type. After that we provide a tight approximation for the optimal transmit power allocation to each BS. Lastly, using the latter two results, the optimal number of BSs that minimize the operational cost is obtained. Numerical results validate the analysis and provide practical insights on optimal BS deployment. We observe that the proposed joint optimization framework, that solves the coverage probability versus operational cost tradeoff, can yield a significant reduction of about 65% in the operational cost as compared to the benchmark fixed allocation scheme.