Information Theory and Reliable Communication

Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next-generation wireless networks As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service requirements In this paper, we present a comprehensive literature review on the research progresses in wireless networks with RF energy harvesting capability, which is referred to as RF energy harvesting networks (RF-EHNs) First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques, and existing applications Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations and review the communication protocols specially designed for RF-EHNs We also explore various key design issues in the development of RF-EHNs according to the network types, ie, single-hop networks, multiantenna networks, relay networks, and cognitive radio networks Finally, we envision some open research directions

Wireless Networks With RF Energy Harvesting: A Contemporary Survey

Mobile-edge computing (MEC) is an emerging paradigm to meet the ever-increasing computation demands from mobile applications. By offloading the computationally intensive workloads to the MEC server, the quality of computation experience, e.g., the execution latency, could be greatly improved. Nevertheless, as the on-device battery capacities are limited, computation would be interrupted when the battery energy runs out. To provide satisfactory computation performance as well as achieving green computing, it is of significant importance to seek renewable energy sources to power mobile devices via energy harvesting (EH) technologies. In this paper, we will investigate a green MEC system with EH devices and develop an effective computation offloading strategy. The execution cost , which addresses both the execution latency and task failure, is adopted as the performance metric. A low-complexity online algorithm is proposed, namely, the Lyapunov optimization-based dynamic computation offloading algorithm, which jointly decides the offloading decision, the CPU-cycle frequencies for mobile execution, and the transmit power for computation offloading. A unique advantage of this algorithm is that the decisions depend only on the current system state without requiring distribution information of the computation task request, wireless channel, and EH processes. The implementation of the algorithm only requires to solve a deterministic problem in each time slot, for which the optimal solution can be obtained either in closed form or by bisection search. Moreover, the proposed algorithm is shown to be asymptotically optimal via rigorous analysis. Sample simulation results shall be presented to corroborate the theoretical analysis as well as validate the effectiveness of the proposed algorithm.

Dynamic Computation Offloading for Mobile-Edge Computing With Energy Harvesting Devices

Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.

Transmission with Energy Harvesting Nodes in Fading Wireless Channels: Optimal Policies

In this paper, we consider a novel mobile relaying technique, where the relay nodes are mounted on unmanned aerial vehicles (UAVs) and hence are capable of moving at high speed. Compared with conventional static relaying, mobile relaying offers a new degree of freedom for performance enhancement via careful relay trajectory design. We study the throughput maximization problem in mobile relaying systems by optimizing the source/relay transmit power along with the relay trajectory, subject to practical mobility constraints (on the UAV’s speed and initial/final relay locations), as well as the information-causality constraint at the relay. It is shown that for the fixed relay trajectory, the throughput-optimal source/relay power allocations over time follow a “staircase” water filling structure, with non-increasing and non-decreasing water levels at the source and relay, respectively. On the other hand, with given power allocations, the throughput can be further improved by optimizing the UAV’s trajectory via successive convex optimization. An iterative algorithm is thus proposed to optimize the power allocations and relay trajectory alternately. Furthermore, for the special case with free initial and final relay locations, the jointly optimal power allocation and relay trajectory are derived. Numerical results show that by optimizing the trajectory of the relay and power allocations adaptive to its induced channel variation, mobile relaying is able to achieve significant throughput gains over the conventional static relaying.

Throughput Maximization for UAV-Enabled Mobile Relaying Systems

This paper considers a two-user Gaussian interference channel with energy harvesting transmitters. Different than conventional battery powered wireless nodes, energy harvesting transmitters have to adapt transmission to availability of energy at a particular instant. In this setting, the optimal power allocation problem to maximize the sum throughput with a given deadline is formulated. The convergence of the proposed iterative coordinate descent method for the problem is proved and the short-term throughput maximizing offline power allocation policy is found. Examples for interference regions with known sum capacities are given with directional water-filling interpretations. Next, stochastic data arrivals are addressed. Finally online and/or distributed near-optimal policies are proposed. Performance of the proposed algorithms are demonstrated through simulations.

Sum-Rate Optimal Power Policies for Energy Harvesting Transmitters in an Interference Channel

We consider an energy harvesting network where the transmitter harvests energy from nature, and the harvested energy can be saved in an imperfect battery which suffers from charging/ discharging inefficiency. In particular, when E units of energy is to be stored in the battery, only ηE units is saved and (1 - η)E is lost due to charging/discharging inefficiency, where 0 ≤ η ≤ 1 represents the storing efficiency. We determine the optimum offline transmit power schedule for such a system for single-user and broadcast channel models, for static and fading channels, with and without a finite battery size. We show that the optimum policy is a double-threshold policy: specifically, we store energy in the battery only when the harvested energy is above an upper threshold, and retrieve energy from the battery only when the harvested energy is below a lower threshold; when the harvested energy is in between these two thresholds, we use it in its entirety in the current slot. We show that the two thresholds remain constant unless the battery is depleted or full. We provide an algorithm to determine the sequence of optimum thresholds. For the case with fading, we develop a directional water-filling algorithm which has a double-threshold structure. Finally, we formulate the online problem using dynamic programming, and numerically observe that the online policy exhibits a double-threshold structure as well.

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Optimum Policies for an Energy Harvesting Transmitter Under Energy Storage Losses

This paper provides a general framework for utility maximization of a wireless network with energy harvesting nodes. The focus is on applying this framework to the single-link problem with an energy harvesting transmitter and an energy harvesting receiver. For the general utility maximization problem, it is shown that if the utility of a network can be expressed instantaneously as a function of the powers of the nodes, then the maximum utility achieving power policy for each node can be found using a water-filling approach for each user. This is achieved by expressing the general utility maximization problem as a pair of nested problems focusing on energy efficiency and adapting to energy harvests separately. The framework extends the previous results on offline optimization of energy harvesting transmitters to networks with all energy harvesting nodes including receivers and relays as well as any network utility, provided that the achieved utility is instantaneous and additive in time. The implications of the energy efficiency problem on the energy harvesting problem are demonstrated over an energy harvesting transmitter-receiver pair, and simulation results are presented to exhibit the performance of the optimal policy along with some alternatives for a range of storage capacities.

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Communicating with energy harvesting transmitters and receivers

Wireless networks composed of energy harvesting devices will introduce several transformative changes in wireless networking as we know it: energy self-sufficient, energy self-sustaining, perpetual operation; reduced use of conventional energy and accompanying carbon footprint; untethered mobility; and an ability to deploy wireless networks in hard-to-reach places such as remote rural areas, within structures, and within the human body. Energy harvesting brings new dimensions to the wireless communication problem in the form of intermittency and randomness of available energy, which necessitates a fresh look at wireless communication protocols at the physical, medium access, and networking layers. Scheduling and optimization aspects of energy harvesting communications in the medium access and networking layers have been relatively wellunderstood and surveyed in the recent paper [1]. This branch of literature takes a physical layer rate-power relationship that is valid in energy harvesting conditions under large-enough batteries and long-enough durations between energy harvests so that information-theoretic asymptotes are achieved, and optimizes the transmit power over time in order to maximize the throughput. Another branch of recent literature aims to understand the fundamental capacity limits, i.e. information-theoretic capacities, of energy harvesting links under smaller scale dynamics, considering energy harvests at the channel use level. This branch necessitates a deeper look at the coding and transmission schemes in the physical layer, and ultimately aims to develop an information theory of energy harvesting communications, akin to Shannon's development of an information theory for average power constrained communications. In this introductory article, we survey recent results in this branch and point to open problems that could be of interest to a broad set of researchers in the fields of communication theory, information theory, signal processing, and networking. In particular, we review capacities of energy harvesting links with infinite-sized, finitesized, and no batteries at the transmitter.

Fundamental limits of energy harvesting communications

This paper considers two-hop communication networks where the transmitters harvest their energy in an intermittent fashion. In this network, communication is carried out by signal cooperation, i.e., relaying. Additionally, the transmitters have the option of transferring energy to one another, i.e., energy cooperation. Energy is partially lost during transfer, exposing a trade-off between energy cooperation and use of harvested energy for transmission. A multi-access relay model is considered and transmit power allocation and energy transfer policies that jointly maximize the sum-rate are found. It is shown that a class of power policies achieves the optimal sum-rate, allowing a separation of optimal energy transfer and optimal power allocation problems. The optimal energy transfer policy is shown to be an ordered node selection, where nodes with better energy transfer efficiency and worse channels transfer all their energy to the relay or other source nodes via the relay. For the special case of single source, the optimal policy requires the direction of energy transfer to remain unchanged unless either node depletes all of its energy. Overall, the findings provide the insight that cooperation of the source nodes by sharing energy with the relay node leads to them indirectly cooperating with each other, and that such cooperation can be carried out in a last-minute fashion.

/pdf/cooperative-energy-harvesting-communications-with-relaying-2dhl8s02ia.pdf

Kaya Tutuncuoglu

Papers

Sum-Rate Optimal Power Policies for Energy Harvesting Transmitters in an Interference Channel

Optimum Policies for an Energy Harvesting Transmitter Under Energy Storage Losses

Communicating with energy harvesting transmitters and receivers

Fundamental limits of energy harvesting communications

Cooperative energy harvesting communications with relaying and energy sharing