Energy Harvesting Wireless Networks Research Articles

Joint power and subchannel allocation regarding energy harvesting in MC-NOMA based wireless networks

We propose resource allocation schemes for a multi-carrier non-orthogonal multiple access (MC-NOMA) system with the consideration of energy harvesting. The resource allocation schemes includes an optimization scheme for maximizing the total system capacity, and an optimization scheme for maximizing the fairness among users, such that both schemes guarantee minimum data rate and harvested energy to each user. Because NOMA is one of the key technologies for enhancing system capacity, we apply a NOMA scheme to an Energy Harvesting Wireless Network (EHWN) to increase the capacity whilst at the same time supplying sufficient harvested energy and data rate for each user. The proposed resource allocation schemes are based on a block coordinate descent (BCD) algorithm that can effectively solve problems in which the objective function is not jointly concave. The performance of each proposed scheme is evaluated using simulations, and we show that the capacity maximization and fairness schemes outperform orthogonal multiple access (OMA) schemes in terms of capacity and fairness, respectively.

Energy Stimulated Time Synchronization for Energy Harvesting Wireless Networks

In this work, we develop a new energy-preserving time synchronization method, called the energy stimulated time sync (ESTS), for ultra-low-power wireless networks with energy harvesting capabilities. Compared with existing methods, ESTS does not rely on timestamp exchange but uses short energy tones to synchronize the nodes within the receiving range. ESTS is a centralized synchronization method based on a fully-passive wake-up radio (WuR) circuit, which can provide a variety of synchronization accuracy to meet different application requirements while consuming much lower energy than timestamp-based synchronization methods. We have implemented ESTS on Microchip ATmega256RFR2 system-on-chip (SoC). According to experiment results, ESTS can achieve an average of 5 ms and 70 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>s time precision for rough synchronization and fine synchronization, respectively. Moreover, the fully-passive WuR circuit consumes zero energy in the idle status. In the entire synchronization process, the rough synchronization only needs to be performed once, and the node consumes as low as 4 <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula>J of energy for each fine synchronization, making ESTS a promising solution for ultra-low-power wireless networks.

Energy Harvesting Rectenna Design for Enhanced Node Lifetime In WSNs

With the rising popularity and advent of many services on Wireless Sensor Networks (WSNs), there is a compelling need to have energy-efficient solutions. The sensors and devices are powered by batteries whose life is limited, and at times it is impossible to replace the batteries, especially in remote applications. In such a scenario, Energy Harvesting (EH) stands as an undisputed candidate for enhancing the network lifetime. Radio Frequency (RF) energy is the most commonly available, ubiquitous, and reliable energy source among all the available energy sources. While RF signal carries both information and energy, EH is possible for long-distance and mobile environments. This work discusses initial research in the domain of EH in wireless networks via Radio Frequency (RF) signals. The paper presents an EH rectenna for energy harvesting over 2.45 GHz (Wi-Fi band). The receiving antenna is designed to pick up the radio signal in the RF range (2.45 GHz) from the free space. The four patch elements design has an antenna substrate made with RT with a dielectric constant of 2.2. The paper presents the simulation results of the basic parameters of the antenna, such as return loss, input impedance, bandwidth, gain, directivity, and efficiency. H-shaped slot antenna and modified H-shaped antenna (with circular slot) are designed with a gain of 8.24 dB and 8.32 dB, return loss of -10 dB and -16 dB, and bandwidth of 64.8 MHz and 868 MHz. The high gain, large bandwidth, properly matched impedance for minimum return loss, and high efficiency of the modified H-shaped patch antenna makes it eligible for energy harvestin.

Power Splitting and Source-Relay Selection in Energy Harvesting Wireless Network

This paper investigates the performance of an energy-harvesting (EH) relay network, where multiple sources communicate with a destination via multiple EH decode-and-forward (DF) relays. The EH relays all equip with a power splitter to divide the received signal power into two parts, which are used for signal processing and information forwarding, respectively. The power splitting ratio depicts the trade-off between the relaying energy and decoding energy. We propose an optimal power splitting and joint source-relay selection (OPS-JSRS) scheme where the optimal power-splitting ratio is obtained and the best source-relay pair is selected to transmit the message. For the purpose of comparison, we present the optimal power splitting and round-robin (OPS-RR) and the traditional power splitting and joint source-relay selection (TPS-JSRS) schemes. The exact and asymptotic closed-form expressions of outage probability for OPS-RR, TPS-JSRS and OPS-JSRS schemes are derived over Rayleigh fading channels . Numerical results show that the outage probability of OPS-JSRS scheme is lower than that of OPS-RR and TPS-JSRS schemes, explaining that the proposed OPS-JSRS scheme outperforms TPS-JSRS and OPS-RR schemes. Additionally, the outage probability performance of OPS-JSRS scheme can be improved by increasing the number of sources and/or relays.

Federated Learning Over Energy Harvesting 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.

A Survey on Energy Harvesting Wireless Networks: Channel Capacity, Scheduling, and Transmission Power Optimization

This paper presents a survey on energy harvesting (EH) wireless communication networks focusing on channel capacity, transmission schemes, and power optimization. While many network researchers focus on energy management policies addressing the intermittency and randomness of the EH processes, but the channel capacity, and transmission power optimization have not been fully explored yet. In this paper, we provide a review and analysis of channel capacity, offline and online transmission schemes, and power optimization from an information theory perspective. By reviewing and analyzing wireless networking literature, we found that EH is a technologically feasible and economically viable paradigm for cost-effectiveness in the design and deployment of next-generation wireless networks. Finally, we identify open research problems and future research directions in the emerging field of EH wireless networks. We expect this study to stimulate more research endeavors to build energy-efficient scalable next-generation wireless network systems.

Threshold-based Wireless-based NOMA Systems over Log-Normal Channels: Ergodic Outage Probability of Joint Time Allocation and Power Splitting Schemes

Due to the development of state-of-the-art fifth-generation communication (5G) and Internet-of-Things (IoT), the demands for capacity and throughput of wireless networks have increased significantly. As a promising solution for this, a radio access technique, namely, non-orthogonal multiple access (NOMA) has been investigated. Particularly, in this paper, we analyse the system performance of a joint time allocation and power splitting (JTAPS) protocol for NOMA-based energy harvesting (EH) wireless networks over indoor scenarios, which we modelled with log-normal fading channels. Accordingly, for the performance analysis of such networks, the analytical expression of a metric so-called “ergodic outage probability” was derived. Then, thanks to Monte Carlo simulations done in Matlab, we are able to see how different EH power splitting (PS) and EH time switching (TS) factors influence the ergodic outage probability. Last, but not least, we plot the simulation results along with the theoretical results for comparison studies.

RF Energy Harvesting Wireless Networks: Challenges And Opportunities

Energy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced.

RF Energy Harvesting Wireless Networks: Challenges And Opportunities

Energy harvesting wireless networks is one of the most researched topics in this decade, both in industry and academia, as it can offer self-sustaining sensor networks. With RF energy harvesting (RF-EH) embedded, the sensors can operate for extended periods by harvesting energy from the environment or by receiving it as an Energy signal from a hybrid base station (HBS). Thus, providing sustainable solutions for managing massive numbers of sensor nodes. However, the biggest hurdle of RF energy is the low energy density due to spreading loss. This paper investigates the RF-EH node hardware and design essentials, performance matrices of RF-EH. Power management in energy harvesting nodes is discussed. Furthermore, an information criticality algorithm is proposed for critical and hazardous use cases. Finally, some of the RF-EH applications and the opportunities of 5G technologies for the RF-EH are introduced.

WUR-TS: Semi-Passive Wake-Up Radio Receiver Based Time Synchronization Method for Energy Harvesting Wireless Networks

WUR-TS: Semi-Passive Wake-Up Radio Receiver Based Time Synchronization Method for Energy Harvesting Wireless Networks

Deep Reinforcement Learning-Assisted Energy Harvesting Wireless Networks.

Heterogeneous ultra-dense networking (HUDN) with energy harvesting technology is a promising approach to deal with the ever-growing traffic that can severely impact the power consumption of small-cell networks. Unfortunately, the amount of harvested energy, which depends on the transmission environment, is highly random and difficult to predict. Since there may be multiple sources of energy in the HUDN, e.g., macro base stations or TV towers, the challenging issue is when and where to harvest energy. Optimally controlling the HUDN can profoundly influence the performance of both data transmission and energy harvesting. However, the working pattern of individual small cell base stations needs to be determined in every time slot. To find an optimal solution in a highly random environment we propose reinforcement learning methods, such as deep deterministic policy gradient (DDPG) and wolpertinger DDPG (W-DDPG). Since the action space is large and discrete for the controlling tasks, a W-DDPG algorithm has been found to be the best approach. The simulation results verify that, compared with the original DDPG algorithm and deep Q-learning, the proposed W-DDPG method can achieve a superior performance in terms of both energy efficiency and throughput.

Relay Selection and Performance Analysis of Wireless Energy Harvesting Networks

We proposed an integrated information relay and wireless power supply assisted RF energy harvesting-based cooperative dual-hope decode-and-forward (DF) relaying communication model. The relay node not only aids the communication between energy constrained source and destination but also supply power to them using time switching (TS) protocol. We also proposed a relay selection protocol where the source is capable of selecting an appropriate relay link on the basis of channel gain condition. The performance of the system in terms of outage probability and achievable ergodic capacity over Rayleigh fading channels are thoroughly analyzed. Closed from analytical expression of outage probability of the considered system is derived and authenticated by the Monte-Carlo simulation result. The results show the impact of the number of relay nodes on outage probability and achievable ergodic capacity. Simulation results also demonstrated the optimum energy harvesting time for which system achieves maximum throughput and minimum outage probability.

Algorithms and methods for energy harvesting in wireless networks: A review

Algorithms and methods for energy harvesting in wireless networks: A review

Wireless energy harvesting networks with multiple filter-and-forward relays

Wireless energy harvesting networks with multiple filter-and-forward relays

Optimal Relaying in Energy Harvesting Wireless Networks With Wireless-Powered Relays

In this paper, we consider a wireless cooperative network with a wireless-powered energy harvesting (EH) relay. The relay employs a time switching (TS) policy that switches between the EH and data decoding (DD) modes. Both energy and data buffers are kept at the relay to store the harvested energy and decoded data packets, respectively. In this paper, we derive static and dynamic TS policies that maximize the system throughput or minimize the average transmission delay. In particular, in the static policies, the EH or DD mode is selected with a pre-determined probability. In contrast, in a dynamic policy, the mode is selected dynamically according to the states of data and energy buffers. We prove that the throughput-optimal static and dynamic policies keep the relay data buffer at the boundary of stability. More specifically, we show that the throughput-optimal dynamic policy has a threshold-based structure. Moreover, we prove that the delay-optimal dynamic policy is threshold-based and keeps at most one packet at the relay. We notice that unlike the static case, the delay-optimal and throughput-optimal dynamic policies coincide in most cases. Finally, through extensive numerical results, we demonstrate the efficiency of the optimal dynamic policies compared with the static ones.

Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

Internet of Things (IoT) devices can apply mobile-edge computing (MEC) and energy harvesting (EH) to provide the satisfactory quality of experiences for computation intensive applications and prolong the battery lifetime. In this article, we investigate the computation offloading for IoT devices with energy harvesting in wireless networks with multiple MEC devices such as base stations and access points, each with different computation resource and radio communication capability. We propose a reinforcement learning based computation offloading framework for an IoT device to choose the MEC device and determine the offloading rate according to the current battery level, the previous radio bandwidth to each MEC device and the predicted amount of the harvested energy. A "hotbooting" Q-learning based computation offloading scheme is proposed for an IoT device to achieve the optimal offloading performance without being aware of the MEC model, the energy consumption and computation latency model. We also propose a fast deep Q-network (DQN) based offloading scheme, which combines the deep learning and hotbooting techniques to accelerate the learning speed of Q-learning. We show that the proposed schemes can achieve the optimal offloading policy after sufficiently long learning time and provide their performance bounds under two typical MEC scenarios. Simulations are performed for IoT devices that use wireless power transfer to capture the ambient radio-frequency signals to charge the IoT batteries. Simulation results show that the fast DQN-based offloading scheme reduces the energy consumption, decreases the computation delay and the task drop ratio, and increases the utility of the IoT device in dynamic MEC, compared with the benchmark Q-learning based offloading.

Energy Harvesting Networks: Energy Versus Data Cooperation

Data cooperation and energy cooperation have been recently considered for energy-harvesting wireless networks to enhance their performance. In this letter, we investigate the performance tradeoff of data cooperation versus energy cooperation for a three-node relay network, comprised of a source, relay, and destination, and the results can be generalized. In the data cooperation scenario, we use an existing optimal scheme to optimize the system performance. In the energy cooperation scenario, we derive the system performance and optimize it over the amount of energy transferred from the relay to the source. We show that the best cooperation technique depends on the energy harvesting and energy depletion rates of the relay and the energy transfer efficiency.

Hybrid protocol for wireless energy harvesting network over log‐normal fading channel

In a wireless energy harvesting network with an energy-constrained decode and forward relay node, the authors propose a hybrid protocol (HP) which is a combination of power splitting relaying (PSR) and time switching relaying (TSR). The authors derive the analytical outage probability expressions of the HP over log-normal fading channel. The numerical results show that the HP scheme significantly outperforms the PSR and TSR schemes in terms of outage probability performance.

Optimum transmission policy for remote state estimation with opportunistic energy harvesting

An attractive research direction in energy harvesting wireless networks is to balance the energy transfer and data transmission concurrently. In this paper, we consider the scenario that a transmitter can send the state information of a physical process to a remote estimator or deliver the energy to an energy receiver. It is assumed that the communication channels between the transmitter and the remote estimator and energy receiver sides are block fading. We formulate a problem that weighs the estimation error at the remote estimator side with the harvested energy at the energy receiver side. For the case that the realtime channel state information is known by the transmitter (called full channel state information and FCSI for short), we respectively find out the optimal transmission power switch policy and power split policy. Moreover, these results are further generalized to the case that the realtime channel state information is unknown and the statistical characteristics of the communication channels are known by the transmitter (called partial channel state information and PCSI for short). Finally, simulation examples are presented to demonstrate the effectiveness of our proposed transmission policies.

Optimal User Scheduling in Energy Harvesting Wireless Networks

We consider a wireless network where multiple energy harvesting transmitters communicate with the common receiver in a time-sharing manner. In each slot, a transmitter can either harvest energy or send its data to the receiver. Given a time deadline, the goal is to maximize the sum rate of transmitters under random energy arrivals with both perfect and imperfect channel state information at the receiver. The original sum-rate maximization (SRM) problem is a non-convex mixed integer non-linear program (MINLP). To obtain the optimal scheduling policy, we first reduce the original optimization problem to a convex MINLP and solve it using the generalized Benders decomposition algorithm. We observe that the SRM problem results in an unfair rate allocation among transmitters, i.e., the transmitter closer to the receiver achieves a higher rate than that by the transmitter farther from the receiver. Hence, to induce fairness among transmitters, we consider the minimum-rate maximization (MRM) problem. For the bounded channel estimation error, we obtain a robust scheduling policy by solving the worst-case SRM and MRM problems. Finally, we compare the proposed policies with myopic policies studied in the literature and show that the former outperform the latter in terms of achievable rates.