Energy Harvesting Communication Research Articles

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

UAV Trajectory, User Association, and Power Control for Multi-UAV-Enabled Energy-Harvesting Communications: Offline Design and Online Reinforcement Learning

Smart Energy Borrowing and Relaying in Wireless-Powered Networks: A Deep Reinforcement Learning Approach

Wireless energy harvesting (EH) communication has long been considered a sustainable networking solution. However, it has been limited in efficiency, which has been a major obstacle. Recently, strategies such as energy relaying and borrowing have been explored to overcome these difficulties and provide long-range wireless sensor connectivity. In this article, we examine the reliability of a wireless-powered communication network by maximizing the net bit rate. To accomplish our goal, we focus on enhancing the performance of hybrid access points and information sources by optimizing their transmit power. Additionally, we aim to maximize the use of harvested energy, by using energy-harvesting relays for both information transmission and energy relaying. However, this optimization problem is complex, as it involves non-convex variables and requires combinatorial relay selection indicator optimization for decode and forward (DF) relaying. To simplify this problem, we utilize the Markov decision process and deep reinforcement learning framework based on the deep deterministic policy gradient algorithm. This approach enables us to tackle this non-tractable problem, which conventional convex optimization techniques would have difficulty solving in complex problem environments. The proposed algorithm significantly improved the end-to-end net bit rate of the smart energy borrowing and relaying EH system by 13.22%, 27.57%, and 14.12% compared to the benchmark algorithm based on borrowing energy with an adaptive reward for Quadrature Phase Shift Keying, 8-PSK, and 16-Quadrature amplitude modulation schemes, respectively.

Resource Allocation Policies for Battery Constrained Energy Harvesting Communication Systems With Co-Channel Interference

This paper studies resource allocation policies for energy harvesting (EH) multi-user multiple input single output (MU- MISO) communication systems. The multi-antenna EH base station (BS) is equipped with a limited-capacity battery. Though employing the multi-antenna at the BS provides the channel diversity, this leads to the co-channel interference which makes the resource allocation problem hard to solve. For this challenging scenario, we first consider off-line policies based on full channel state information (CSI) and energy arrival information (EAI) to obtain the best performance for any feasible resource allocation policies. We propose an iterative algorithm using generalized linear fractional programming to obtain an optimal policy. To achieve a low-complexity sub-optimal policy, we propose another iterative algorithm using the successive convex approximation. Based on the off-line policies, we develop on-line policies in which only statistical CSI and EAI are available. The complexity of the proposed policies is derived. Finally, simulation results evaluate the performance of the proposed approaches and show that the proposed polices outperform the benchmark.

Wireless light energy harvesting and communication in a waterproof GaN optoelectronic system

Wireless technologies can be used to track and observe freely moving animals. InGaN/GaN light-emitting diodes (LEDs) allow for underwater optical wireless communication due to the small water attenuation in the blue-green spectrum region. GaN-based quantum well diodes can also harvest and detect light. Here, we report a monolithic GaN optoelectronic system (MGOS) that integrates an energy harvester, LED and SiO2/TiO2 distributed Bragg reflector (DBR) into a single chip. The DBR serves as waterproof layer as well as optical filter. The waterproof MGOS can operate in boiling water and ice without external interconnect circuits. The units transform coded information from an external light source into electrical energy and directly activate the LEDs for illumination and relaying light information. We demonstrate that our MGOS chips, when attached to Carassius auratus fish freely swimming in a water tank, simultaneously conduct wireless energy harvesting and light communication. Our devices could be useful for tracking, observation and interacting with aquatic animals.

On Optimal Power Control for Energy Harvesting Communications With Lookahead

Consider the problem of power control for an energy harvesting communication system, where the transmitter is equipped with a finite-sized rechargeable battery and is able to look ahead to observe a fixed number of future energy arrivals. An implicit characterization of the maximum average throughput over an additive white Gaussian noise channel and the associated optimal power control policy is provided via the Bellman equation under the assumption that the energy arrival process is stationary and memoryless. A more explicit characterization is obtained for the case of Bernoulli energy arrivals by means of asymptotically tight upper and lower bounds on both the maximum average throughput and the optimal power control policy. Apart from their pivotal role in deriving the desired analytical results, such bounds are highly valuable from a numerical perspective as they can be efficiently computed using convex optimization solvers.

Secure Wirelessly Powered Networks at the Physical Layer: Challenges, Countermeasures, and Road Ahead

Harvesting wireless power to energize miniature devices has been envisioned as a promising solution to sustain future-generation energy-sensitive networks, e.g., Internet-of-Things systems. However, due to the limited computing and communication capabilities, wirelessly powered networks (WPNs) may be incapable of employing complex security practices, e.g., encryption, which may incur considerable computation and communication overheads. This challenge makes securing energy harvesting communications an arduous task and, thus, limits the use of WPNs in many high-security applications. In this context, security at the physical layer (PHY) that exploits the intrinsic properties of the wireless medium to achieve secure communication has emerged as an alternative paradigm. This article first introduces the fundamental principles of primary PHY attacks, covering jamming, eavesdropping, and detection of covert, and then presents an overview of the prevalent countermeasures to secure both active and passive communications in WPNs. Furthermore, a number of open research issues are identified to inspire possible future research.

SWIPT-Enabled Multiple Access Channel: Effects of Decoding Cost and Non-Linear EH Model

We studied power splitting-based simultaneous wireless information and power transfer (PS-SWIPT) in multiple access channels (MACs), considering the decoding cost and non-linear energy harvesting (EH) constraints at the receiving nodes to study practical limitations of an EH communication system. Under these restrictions, we formulated and analyzed the achievable rate and maximum departure regions in two well-studied scenarios, i.e., a classical PS-SWIPT MAC and a PS-SWIPT MAC with user cooperation. In the classical PS-SWIPT MAC setting, closed-form expressions for the optimal values of the PS factors are derived for two fundamental decoding schemes: simultaneous decoding and successive interference cancellation. In the PS-SWIPT MAC with user cooperation, the joint optimal power allocation for users as well as the optimal PS factor are derived. This reveals that one decoding scheme outperforms the other in the classical PS-SWIPT MAC, depending on the function type of the decoding cost. Finally, it is shown that the cooperation between users can potentially boost the performance of a PS-SWIPT MAC under decoding cost and non-linear EH constraints. Moreover, effects of the decoding cost functions, non-linear EH model and channel quality between the users are studied, and performance characteristics of the system are discussed.

On the Optimality of the Greedy Policy for Battery Limited Energy Harvesting Communications

Consider a battery limited energy harvesting communication system with online power control. Assuming independent and identically distributed (i.i.d.) energy arrivals and the harvest-store-use architecture, it is shown that the greedy policy achieves the maximum throughput if and only if the battery capacity is below a certain positive threshold that admits a precise characterization. Simple lower and upper bounds on this threshold are established. The asymptotic relationship between the threshold and the mean of the energy arrival process is analyzed for several examples.

Online Power Control and Optimization for Energy Harvesting Communication System Based on State of Charge

In this paper, the online power control problem for energy harvesting wireless communication system with a finite storage capacity battery is addressed, where the channel state and energy harvesting rate are both unknown. A low complexity algorithm based online convex optimization is proposed to guarantee energy availability of energy harvesting node and maximize average long-term throughput. The proposed algorithm restricts maximum transmission power with the information of state of charge, and allocates transmission power based on historical information. In addition, energy availability constraint is given by rigorous theoretical analysis to guarantee the optimization of average long-term throughput. Simulations have been conducted to demonstrate the effectiveness of the algorithm without considering probability distribution of energy arrival or channel coefficients. The proposed algorithm outperforms counterparts in different energy harvested rates.

Shallow Reinforcement Learning for Energy Harvesting Communications With Imperfect Channel Knowledge

This study aims to address the power allocation problem to maximize the sum of the generalized mutual information, which refers to the achievable rate with imperfect channel state information, through a reinforcement learning (RL) approach in energy harvesting communications. In contrast to the conventional deep RL applications, which incur a large computational load on the devices due to the use of deep neural networks, we adopt shallow RL architectures involving the optimal structural properties pertaining to the optimal power allocation policy. To design the shallow architectures that can fully capture the desired power allocation policy, we derive the partial monotonicity of and bounds on the policy and value functions. These structural properties represent mathematical bases on which to construct the shallow architecture. We use a deterministic policy gradient method with monotonically shape-constrained approximators that allow us to avoid using overly complicated deep neural networks, which are not suitable for low-power devices. Through various experiments, we visualize the solutions derived from the proposed shallow architectures and demonstrate that the proposed method outperforms existing power allocation policies and exhibits a greater robustness due to optimal structural properties.

Neural-Network-Based Power Control Prediction for Solar-Powered Energy Harvesting Communications

In this article, we design neural network (NN)-based transmit power control prediction for solar-powered energy harvesting (EH) communications under single-user (SU) and multiuser (MU) scenarios with real solar data. Although the directional water filling (DWF) is known as the optimal scheme for the SU case, it necessitates the full (past and future) channel state information (CSI) and energy state information (ESI) in realizing the optimal solution over a time period. To conquer this impracticality, an SU-GreenPCNet, which only requires the past short-term CSI and ESI for predicting the SU transmit power, is proposed and trained with the historical solar data. For the MU case, two iterative algorithms, namely, weighted-sum minimum mean-square error (WMMSE) and iterative DWF (IDWF), are investigated to tackle the original nonconvex power control problem when the full state knowledge is assumed to be known in advance. The solutions with the historical solar data are then served as benchmarks for designing two MU-GreenPCNets. As an extension of the SU-GreenPCNet, a centralized scheme is proposed at the central controller for jointly determining the MU transmit power values based on the past short-term state knowledge of all users. A distributed scheme is further investigated to reduce the signaling overhead, in which each transmitter merely utilizes the past short-term CSI, ESI, and MU interference (MUI) knowledge associated with its user pair. The simulation results show that the proposed power control prediction schemes can pragmatically achieve satisfied sum rate performance in both SU and MU scenarios, as compared with the benchmark schemes.

Multiple Access Control in a Centralized Full-Duplex Cognitive Machine Type Network with RF Energy Harvesting

Machine type communication connecting machines in the Internet of Things (IoT) brings massive traffic and rapidly increases the demand for radio spectrum in wireless networks. To transmit the traffic in limited spectra, a time division multiple access (TDMA) based interface with a reservation scheme, which is spectrum-efficient, energy-efficient, and deployment-economical, was proposed in cognitive radio based machine type communication. Since radio frequency (RF) energy harvesting is a way to charge stand-alone or low-power machines in IoT and full-duplex communication potentially doubles spectral efficiency, this paper proposes a multiple access control that incorporates RF energy harvesting and full-duplex communication into the TDMA based interface with a reservation scheme. The proposed multiple access control elaborately uses the full-duplex functionality with a mechanism of detecting primary users to appropriately turn on/off RF charging such that the trade-off between the interference of primary users and RF charging is balanced. Extensive simulation results show that the proposed multiple access control produces high system throughput while the interference of primary users is kept at a low level.

COMPACT UWB SLOTTED MONOPOLE ANTENNA WITH DIPLEXER FOR SIMULTANEOUS MICROWAVE ENERGY HARVESTING AND DATA COMMUNICATION APPLICATIONS

COMPACT UWB SLOTTED MONOPOLE ANTENNA WITH DIPLEXER FOR SIMULTANEOUS MICROWAVE ENERGY HARVESTING AND DATA COMMUNICATION APPLICATIONS

Wireless‐powered CR‐IoT with ambient backscattering: a new transmission mode

In this study, a new hybrid secondary transmitter (ST) mode is proposed for cognitive radio Internet of Things (CR-IoT) networks with energy harvesting (EH) and ambient backscatter (AmBack) communication capabilities. The authors firstly describe the proposed hybrid mode with wireless-powered ST. The ST, consisting of EH, Amback, and conventional active transmission modules, adapts itself under different primary user operation states, i.e. active or idle. The overall throughput of the secondary system is formulated and the system settings for achieving optimal performance are derived and validated through numerical simulations.

RF energy modelling using machine learning for energy harvesting communications systems

SummaryMachine learning (ML) theories and methods are mainly based on probability theory and statistics. It is a very powerful tool for data modelling. On the other hand, energy harvesting has been regarded as a viable solution to extending battery lifetime of wireless sensor network. Motivated by these, modelling of the radio frequency (RF) energy available to the wireless nodes is required for efficient operation of wireless networks. In this work, we will use different ML algorithms to model the RF energy data for efficient operation of energy harvesting communication systems. Four ML algorithms are studied and compared in terms of the accuracy for RF energy modelling using the energy data in the band between 1805 and 1880 MHz. The results show that linear regression (LR) has the highest accuracy and the most stable performance, while decision tree is the worst model. Also, in terms of the operation efficiency of the system, LR has the best performance, followed by support vector machine and random forest algorithm.

Joint EH Time and Transmit Power Optimization Based on DDPG for EH Communications

Energy management and power allocation policy is considered for energy harvesting (EH) communications. In this letter, we propose a joint optimization problem with the continuous EH time and transmit power to maximize the long-term throughput based on deep deterministic policy gradient (DDPG). However, the joint optimization problem leads to a large continuous action space. In order to reduce the dimension of action space, we present a deep reinforcement learning (DRL) framework by combining DDPG and convex program. The original problem is decomposed into two-layer optimization subproblems by using the primal decomposition method. The primary problem can be solved by DDPG with a low-dimensional action space. The lower-layer subproblem can be solved by using the existing convex toolbox. Numerical simulation results show that, compared with the existing energy management or power allocation policies for EH communications, the proposed DRL framework can achieve higher long-term throughput.

Battery-Level-Triggered Transmit Power Control for Energy Harvesting Communications

We consider a continuous-time energy harvesting (EH) communication system consisting of an EH transmitter with random lifetime and a receiver. We assume that neither the fading channel state nor the EH state is available to the transmitter and the transmitter can only observe its battery level, based on which it adjusts its transmit power. Specifically, we quantize the battery capacity of the transmitter into a number of levels, and whenever the energy in the battery hits a certain level, the EH transmitter updates its transmit power. Our main objective is to find the optimal battery-level-triggered (BLT) control policy to maximize the expected total throughput of the transmitter in its lifetime. We model the system as an extended two-dimensional stochastic fluid model (2D-SFM), and derive the Laplace-Stieltjes Transform (LST) matrices of the imbedded process on the decision time sequence, based on which, we formulate the power control problem as a Markov decision process (MDP). We obtain the BLT control policy and the maximum expected throughput by solving the 2D-SFM induced MDP. We evaluate the performance of the BLT policy by comparing it with the conventional uniform-in-time control policies, and results show that the proposed BLT power control policy provides significantly higher expected throughput under the same total energy consumption and the same average control frequency.

A Maximin Optimal Online Power Control Policy for Energy Harvesting Communications

A general theory of online power control for discrete-time battery limited energy harvesting communications is developed, which leads to, among other things, an explicit characterization of a maximin optimal policy. This policy only requires the knowledge of the (effective) mean of the energy arrival process and maximizes the minimum asymptotic expected average reward (with the minimization taken over all energy arrival distributions of a given (effective) mean). Moreover, it is universally near optimal and has a strictly better worst-case performance as well as a strictly improved lower multiplicative factor in comparison with the fixed fraction policy proposed by Shaviv and \"{O}zg\"{u}r when the objective is to maximize the throughput over an additive white Gaussian noise channel. The competitiveness of this maximin optimal policy is also demonstrated via numerical examples.

A Nonlinear Recursive Model Based Optimal Transmission Scheduling in RF Energy Harvesting Wireless Communications

The transmission scheduling is a critical problem in radio frequency (RF) energy harvesting communications. Existing transmission strategies in an RF-based energy harvesting system is mainly based on a classic model, in which the data transmission is scheduled in a fixed feasible energy tunnel. In this paper, we re-examine the classic energy harvesting model and show through the theoretical analysis and experimental results that the bounds of feasible energy tunnel are dynamic, which can be affected by the transmission scheduling due to the impact of residual energy on the harvested one. To describe a practical energy harvesting process more accurately, a new model is proposed by adding a feedback loop that reflects the interplay between the energy harvest and the data transmission. Furthermore, to improve network performance, we revisit the design of an optimal transmission scheduling strategy based on the new model. To handle the challenge of the endless feedback loop in the new model, a recursive algorithm is developed. The simulation results reveal that the new transmission scheduling strategy can balance the efficiency of energy reception and energy utilization regardless of the length of energy packets, achieving improved throughput performance for wireless communications.

Performance Analysis of Energy Harvesting-based Multiple Antenna and Multiple Relays Networks

Harvesting regenerable energy from the surrounding environment is an effective way to solve the energy limitation of wireless communication, and energy harvesting communication has become one of the key technologies of 5G. In this paper, based on energy harvesting technology, we study the multiple antenna multiple relaying networks under different relay selection schemes. We design the Maximal ratio combining (MRC) techniques to enhance the system performance under the situation of partial and opportunistic relay selection scheme being used. On this foundation, we investigate the effects of relay selection scheme, multi-antenna technology and number of relays on the multiple relay network. At the same time, we obtain the closed-form expressions for the outage probability of the multiple relaying networks. In addition, Monte Carlo simulation results and intuitive discussions are provided to show the correctness of the analytical results.