Energy Harvesting Communication Networks Research Articles

Energy harvesting half-duplex AF power splitting protocol relay network over rician channel in case of maximizing capacity

In this letter, we propose a novel power splitting protocol for energy harvesting half-duplex AF relaying communication systems. In our proposed system, the relay harvests energy from the source transmissions, by employing adaptive PS protocol, for powering the retransmissions to the destination. The proposed model system is investigated in cases maximize and non-maximize ergodic capacity. Firstly, we perform the analytical mathematical analysis for deriving the integral closed-form expression of the outage probability and the ergodic capacity. Then, the analytical analysis of the system performance can be convinced by Monte-Carlo simulation with helping Mat Lab software. Finally, the numerical analysis provides practical insights into the effect of various system parameters on the system performance of the proposed system. This paper can be considered as a recommendation for the energy harvesting communication network.

Timely Updates in Energy Harvesting Two-Hop Networks: Offline and Online Policies

A two-hop energy harvesting communication network is considered, in which measurement updates are transmitted by a source to a destination through an intermediate relay. Updates are to be sent in a timely fashion that minimizes the age of information, defined as the time elapsed since the most recent update at the destination was generated at the source. The source and the relay communicate using energy harvested from nature, which is stored in infinite-sized batteries. Both nodes use fixed transmission rates, and hence updates incur fixed delays (service times). Two problems are formulated: an offline problem, in which the energy arrival information is known a priori, and an online problem, in which such information is revealed causally over time. In both problems, it is shown that it is optimal to transmit updates from the source just in time as the relay is ready to forward them to the destination, making the source and the relay act as one combined node. A recurring theme in the optimal policy is that updates should be as uniformly spread out over time as possible, subject to energy causality and service time constraints. This is perfectly achieved in the offline setting, and is achieved almost surely in the online setting by a best effort policy.

When is it optimal for the event-triggered strategy in energy harvesting transmission

The energy harvesting network in mobile environment is promising for sustainable environment and green communication. How to efficiently use the harvested energy is a key to improve the performance of energy harvesting communication network. This paper considers a point-to-point communication scenario where a static sensor harvests energy and transmits information to a moving agent. From the viewpoint of information theory, we present an optimal power allocation (PA) solution to achieve the maximum channel service. We prove that using a water-filling transmission power, during a single transmission timeslot, the energy depletion time is uniquely determined by the initial energy in the sensor regardless of the energy-harvesting power. On the other hand, the energy depletion time is a determinant of whether the energy is exhausted at the end of the transmission timeslot. Based on the obtained results, an event-triggered transmission strategy is proposed where a sensor harvests energy to a certain amount so as to trigger information transmission towards the moving agent. Some numerical results are provided to confirm the theoretical analysis and our proposed event-triggered implementation approach.

Stochastic Routing and Scheduling Policies for Energy Harvesting Communication Networks

In this paper, we study the joint routing-scheduling problem in energy harvesting communication networks. Our policies, which are based on stochastic subgradient methods on the dual domain, act as an energy harvesting variant of the stochastic family of backpresure algorithms. Specifically, we propose two policies: (i) the Stochastic Backpressure with Energy Harvesting (SBP-EH), in which a node's routing-scheduling decisions are determined by the difference between the Lagrange multipliers associated to their queue stability constraints and their neighbors'; and (ii) the Stochastic Soft Backpressure with Energy Harvesting (SSBP-EH), an improved algorithm where the routing-scheduling decision is of a probabilistic nature. For both policies, we show that given sustainable data and energy arrival rates, the stability of the data queues over all network nodes is guaranteed. Numerical results corroborate the stability guarantees and illustrate the minimal gap in performance that our policies offer with respect to classical ones which work with an unlimited energy supply.

Asymptotically Optimal Power Allocation for Energy Harvesting Communication Networks

For a general energy harvesting (EH) communication network, i.e., a network where the nodes generate their transmit power through EH, we derive the asymptotically optimal online power allocation solution that optimizes a general utility function when the number of transmit time slots, $N$ , and the battery capacities of the EH nodes, $B_{\rm max}$ , satisfy $N\rightarrow \infty$ and $B_{\rm max}\rightarrow \infty$ . The considered family of utility functions is general enough to include the most important performance measures in communication theory, such as the average data rate, outage probability, average bit-error probability, and average signal-to-noise ratio. The proposed power allocation solution is very simple. Namely, the asymptotically optimal power allocation for the EH network is identical to the optimal power allocation for an equivalent non-EH network whose nodes have infinite energy available but their average transmit power is constrained to be equal to the average harvested power and/or the maximum average transmit power of the corresponding nodes in the EH network. Moreover, the maximum average performance of a general EH network converges to the maximum average performance of the corresponding equivalent non-EH network, when $N\rightarrow \infty$ and $B_{\rm max}\rightarrow \infty$ . Although the proposed solution is asymptotic in nature, it is applicable to EH systems transmitting in a large but finite number of time slots and having a battery capacity much larger than the average harvested power and/or the maximum average transmit power.

Optimal Energy and Data Routing in Networks With Energy Cooperation

We consider the delay minimization problem in an energy harvesting communication network with energy cooperation. In this network, nodes harvest energy from nature for use in data transmission, and may transfer a portion of their harvested energies to neighboring nodes through energy cooperation. For fixed data and energy routing topologies, we determine the optimum data rates, transmit powers and energy transfers, subject to flow and energy conservation constraints, in order to minimize the network delay. We start with a simplified problem with fixed data flows and optimize energy management at each node for the case of a single energy harvest per node. This is tantamount to distributing each node's available energy over its outgoing data links and energy transfers to neighboring nodes. For this case, with no energy cooperation, we show that each node should allocate more power to links with more noise and/or more data flow. In addition, when there is energy cooperation, our numerical results indicate that, energy is routed from nodes with lower data loads to nodes with higher data loads. We extend this setting to the case of multiple energy harvests per node over time. In this case, we optimize each node's energy management over its outgoing data links and its energy transfers to neighboring nodes, over multiple time slots. For this case, with no energy cooperation, we show that, for any given node, the sum of powers on the outgoing links is equal to the single-link optimal power over time. Finally, we consider the problem of joint flow control and energy management for the entire network. We determine the necessary conditions for joint optimality of a power control, energy transfer and routing policy. We provide an iterative algorithm that updates the data and energy flows, and power distribution over outgoing data links. We show convergence to a Pareto-optimal operating point.

Dynamic Energy Trading for Energy Harvesting Communication Networks: A Stochastic Energy Trading Game

This paper studies energy-harvesting communication systems in which different energy-harvesting devices (EHDs) can harvest different amounts of energy and transmit different numbers of data packets in different time slots. We introduce a dynamic energy trading framework that allows the EHDs to transfer and trade their harvested energy with each other. The EHDs are divided into two groups: seller EHDs that can harvest more energy than they can use, and buyer EHDs that cannot harvest sufficient energy to support their required communication services. In the proposed framework, the role of each EHD as a seller EHD or a buyer EHD as well as the amount of energy that each EHD can buy or sell to others change over time. Each EHD cannot observe complete information regarding the harvested energy or the number of data packets transmitted by other EHDs. We introduce a simple energy trading scheduling protocol for the EHDs to discover their nearby EHDs and establish energy trading links with each other. We formulate a new game theoretic model called stochastic energy trading game to analyze the dynamic energy trading among EHDs in a stochastic environment. We derive an optimal energy-trading policy for each EHD to sequentially optimize its decisions. We prove that the proposed policy can achieve a stable and optimal sequence of matchings between buyer and seller EHDs. We present numerical results to compare our proposed energy trading policy with an existing transmit packet scheduling approach, under various network settings and conditions.

Optimization of Source/Relay Wireless Networks With Multiuser Nodes

We compute the optimal communication rate achieved by the nodes of a wireless multihop network with arbitrary topology. The network nodes operate in half-duplex mode and generate information that has to be delivered to a gateway node, possibly through a decode-and-forward relaying strategy. Nodes may make use of multiuser processing, thus transmissions from multiple nodes toward the same receiver are allowed. Additionally, nodes may be energy-constrained, as in the case of battery-powered or energy-harvesting communication networks. In such a scenario, we define the possible (and meaningful) network operational states. Then, by solving a linear optimization problem, we select the most efficient network states and for how long the network should work in each of them. More specifically, the resulting communication strategy maximizes the data rate achievable by the network while meeting the constraints that may exist on the node energy consumption. The results we present show how our approach can be effectively used for an optimal design and usage of wireless networks, as well as under which conditions multiuser processing and long-distance communication between nodes are most beneficial.

Bounds to Fair Rate Allocation and Communication Strategies in Source/Relay Wireless Networks

We analyze the achievable data rate of cooperative relaying strategies in networks where nodes operate in half-duplex mode. Nodes have to deliver their data to a gateway, at a certain rate, and may have limited energy capabilities, as in the case of energy-harvesting communication networks. Both the requested data rate and the available energy capabilities may vary from node to node. Under such constraints, we take an information-theoretic approach and derive cut-set upper bounds to the achievable rate. Furthermore, we devise two kinds of communication strategies, each aiming at a different objective. The former ensures a fair rate allocation to the network nodes, but it neglects their energy constraints. The latter does consider energy constraints by meeting the requirements on the average power consumption at each node and by providing fairness in the data rate allocation. We show the performance of the aforementioned communication strategies, highlighting their effectiveness and providing useful insights on the system behavior.