Energy Arrival Rate Research Articles

Enhanced physical layer security for energy harvesting relay‐aided systems using joint relaying scheme

We investigate a dual‐hop cooperative network with a rechargeable source node, a rechargeable half‐duplex hybrid relay node, and a destination node. The network is assumed to be under attack from potential passive eavesdropper. The direct link between the source and destination is considered part of the system. Both the legitimate receiver and the eavesdropper employ maximal ratio combining (MRC) techniques. Batteries at the source and relay are modeled with finite capacities. We characterize the secure throughput of a relay that can dynamically switch between amplify‐and‐forward (AF), decode‐and‐forward (DF), and randomize‐and‐forward (RF) relaying schemes. In addition, the service rates of the queuing systems at the source and the relay as well as the overall secure throughput of the system are derived. Simulation results show how the energy arrival rate at the batteries and the energy consumed in transmitting a packet influence secure throughput. Moreover, we derive asymptotic expression of secure throughput by modeling the queues as M/D/1 queuing systems and show the impact of such modeling on the physical‐layer security performance.

An optimal policy for joint compression and transmission control in delay-constrained energy harvesting IoT devices

Energy-efficient communication remains one of the key requirements of the Internet of Things (IoT) platforms. The concern on energy consumption can be mitigated by exploiting technical ploys to reduce the volume of data for transmission (e.g., via sensing data compression) as well as by resorting to technological advancements (e.g., energy harvesting). However, these mitigating measures carry their own cost, which is the additional complexity of control and optimization in the digital communication chain. In particular, compression ratio is another control knob that needs adjusting besides the usual transmission parameters. Also, with the random and sporadic nature of the harvested energy, the goal shifts from mere energy conservation to judicious consumption of the renewable energy in a foresighted manner. In this paper, we assume an energy-harvesting IoT device that is tasked with (loss-lessly) compressing and reporting delay-constrained sensing events to an IoT gateway over a time-varying wireless channel. We are interested in computing an optimal policy for joint compression and transmission control adaptive to the node’s energy availability, transmission buffer length, as well as its wireless channel conditions. We cast the problem as a Constrained Markov Decision Process (CMDP), and propose a two-timescale model-free reinforcement learning (RL) algorithm that is able to shape the optimal control policy in the absence of the statistical knowledge of the underlying system dynamics. Exhaustive simulation experiments are conducted to investigate the convergence of the learning algorithm, to explore the impacts of different system parameters (such as: the rate of sensing events, the energy arrival rate, and battery capacity) on the performance of the proposed policy, as well as to compare against some baseline schemes.

Information Rates of Energy Harvesting Communications With Intersymbol Interference

We consider energy harvesting communications over an intersymbol interference (ISI) channel corrupted by additive white Gaussian noise. We assume that the energy arrivals at the transmitter, which is equipped with a finite battery, are independent and identically distributed, and provide a method of computing achievable information rates with channel inputs selected from a finite alphabet. We illustrate the methodology via a set of examples quantifying the effects of energy arrival rates as well as battery capacity on the achievable information rates.

Closed-Form Analysis of Non-Linear Age of Information in Status Updates With an Energy Harvesting Transmitter

Timely status updates are crucial to enabling applications in the massive Internet of Things (IoT). This paper measures the data-freshness performance of a status update system with an energy-harvesting transmitter, considering the randomness in information generation, transmission, and energy harvesting. The performance is evaluated by a non-linear function of age of information (AoI) that is defined as the time elapsed since the generation of the most up-to-date status information at the receiver. The system is formulated as two queues with status packet generation and energy arrivals both assumed to be Poisson processes. With negligible service time, both first-come-first-served (FCFS) and last-come-first-served (LCFS) disciplines for arbitrary buffer and battery capacities are considered, and a method for calculating the average penalty with non-linear penalty functions is proposed. The average AoI, the average penalty under exponential penalty function, and the AoI’s threshold violation probability are obtained in a closed form. When the service time is assumed to follow exponential distribution, a matrix geometric method is used to obtain the average peak AoI. The results illustrate that under the FCFS discipline, the status update frequency needs to be carefully chosen according to the service rate and energy arrival rate in order to minimize the average penalty.

Energy Optimization With Higher Information Quality for SHM Application in Wireless Sensor Networks

Energy is an important resource in wireless sensor nodes which gets depleted soon, especially in harsh environments, where the sensor’s batteries once deployed cannot be reached easily for replacement or recharging activities. Therefore, designing energy-efficient routing protocol for wireless sensor networks is the objective for the applications like structural health monitoring (SHM), military surveillance, and so on. In this paper, energy optimization is performed by the clustering technique. In clustering, the energy harvesting sensor nodes, which are close to the sink and having a higher probability of energy arrival rate, are elected as cluster head nodes. The remaining nonelected node reserves a portion of their harvested energy to be used by them when they are elected as cluster heads. Thus, utilizing this reserved harvested energy, the cluster head nodes can survive longer. Routing the data from cluster head nodes to the sink is done with the shortest path available. Also, sensor node placement optimization is important in SHM applications, as if the desired information quality is achieved with less number of sensor nodes, then it is a cost-effective method. Thus, using the exhaustive search method, the sensor node location optimization is performed in the study presented in this paper.

Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels

In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since the optimization problem is non-convex and difficult to solve directly, by considering the network with the relatively high signal-to-interference-plus-noise ratio (SINR), the non-convex optimization problem can be transformed into a convex optimization problem by convex approximation. We attain the properties of the optimal solution by Lagrange duality and solve the convex optimization problem by the CVX solver. The experimental results demonstrate that the total delay of the energy harvesting WSNs with interference channels is more than that in the orthogonal channel; the total network delay increases with the increasing data flow for the fixed energy arrival rate; and the energy transfer can help to decrease the total delay.

Online Power Control for Block i.i.d. Energy Harvesting Channels

We study the problem of online power control for energy harvesting communication nodes with random energy arrivals and a finite battery. We assume a block i.i.d. stochastic model for the energy arrivals, in which the energy arrivals are constant for a fixed duration ${T}$ , but are independent across different blocks, drawn from an arbitrary distribution. This model serves as a simple approximation to a random process with coherence time ${T}$ . We propose a simple online power control policy, and prove that its performance gap to the optimal throughput is bounded by a constant which is independent of the parameters of the problem. This also yields a simple formula for the approximately optimal long-term average throughput, which sheds some light on the qualitative behavior of the throughput and how it depends on the coherence time of the energy arrival process. Our results show that, perhaps counter-intuitively, for a fixed mean energy arrival rate the approximate throughput decreases with increasing coherence time ${T}$ of the energy arrival process. In particular, the battery size needed to approach the AWGN capacity of the channel increases linearly with the coherence time of the process. Finally, we show that our results can provide an approximation to the information-theoretic capacity of the same channel.

On the Time Scales of Energy Arrival and Channel Fading in Energy Harvesting Communications

In wireless communication systems powered by harvested energy, besides the channel fading, there exists another dimension of dynamics, i.e., energy arrival variation. In this paper, we propose a framework for analyzing the energy harvesting powered wireless transmissions where the energy arrival variations and the channel fading are of different timescales. The energy arrival rate changes every ${N (N \ge 1)}$ time slots, and the channel state changes every ${M (M \ge 1)}$ slots. We consider a power allocation problem among the time slots, which can be formulated as a Markov decision process and solved by dynamic programming (DP) algorithm. For the special case that ${M=1}$ , a low-complexity two-stage DP algorithm is proposed, which decouples the original problem into inner and outer sub-problems. The inner problem deals with the power allocation in channel fading timescale in every ${N}$ slots where the energy arrival rate keeps constant, and the outer problem deals with the energy management when the energy arrival rate changes. Numerical simulations show that the average data rate decreases as ${N}$ or ${M}$ increases, and the two-stage DP algorithm can perform close to the DP optimal algorithm.

Capacity of Energy Harvesting Binary Symmetric Channels With a $(\sigma ,\rho )$ -Power Constraint

Capacity of energy harvesting communications with deterministic energy arrival and finite battery size is investigated. An abstraction of the physical layer is considered, where binary sequences are transmitted through a binary symmetric channel, and a cost function is associated with the transmission of each symbol. Upper and lower bounds on the channel capacity are derived for the general case by studying the normalized exponent of the cardinality of the set of feasible input sequences. Several upper bounds on the exponent are proposed by studying supersets of the feasible set. Lower bounds are derived by applying the binary entropy-power inequality and by using specific signaling schemes based on a save-and-transmit strategy. Numerical results are presented for several values of the energy arrival rate and battery size, validating the usefulness of the capacity bounds established for the energy harvesting channels.

Optimal Spectrum Sensing-Access Policy in Energy Harvesting Cognitive Radio Sensor Networks

In this paper, we introduce energy harvesting into a cognitive radio sensor network to power the network with renewable energy sources so as to achieve self-sustainability of energy-limited sensors. In our work, the cognitive radio technology enable sensors access to the underutilized spectrum for the purpose of coping with the spectrum-scarcity problem in the unlicensed band. Using centralized cooperative spectrum sensing, a set of cognitive sensors is chosen from candidate sensors with different received primary users’ signal powers and energy-arrival rates. After detecting the state of a primary channel, we also need to determine which cognitive sensor can get to access the primary channel as well as the power level to be used upon the transmission. The above sensing-access design problem is formulated as an infinite-horizon partially observable Markov decision process, in which the primary goal is to maximize the long-term expected throughput. Through using a value iteration approach, we propose an optimal sensing-access policy. At last, numerical results are presented to verify the superiority of our proposed policy to the existing policy.

User Scheduling and Resource Allocation in HetNets With Hybrid Energy Supply: An Actor-Critic Reinforcement Learning Approach

Densely deployment of various small-cell base stations in cellular networks to increase capacity will lead to heterogeneous networks (HetNets), and meanwhile, embedding the energy harvesting capabilities in base stations as an alternative energy supply is becoming a reality. How to make efficient utilization of radio resource and renewable energy is a brand-new challenge. This paper investigates the optimal policy for user scheduling and resource allocation in HetNets powered by hybrid energy with the purpose of maximizing energy efficiency of the overall network. Since wireless channel conditions and renewable energy arrival rates have stochastic properties and the environment’s dynamics are unknown, the model-free reinforcement learning approach is used to learn the optimal policy through interactions with the environment. To solve our problem with continuous-valued state and action variables, a policy-gradient-based actor-critic algorithm is proposed. The actor part uses the Gaussian distribution as the parameterized policy to generate continuous stochastic actions, and the policy parameters are updated with the gradient ascent method. The critic part uses compatible function approximation to estimate the performance of the policy and helps the actor learn the gradient of the policy. The advantage function is used to further reduce the variance of the policy gradient. Using the numerical simulations, we demonstrate the convergence property of the proposed algorithm and analyze network energy efficiency.

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.

Energy-Arrival-Aware Detection Threshold in Wireless-Powered Cognitive Radio Networks

Traffic patterns associated with different primary user (PU) channels may provide different spectral access and energy harvesting opportunities in wireless-powered cognitive radio networks (WP-CRNs). Considering this, we propose traffic-specific optimal spectrum sensing policy such that the expected transmission rate of secondary user (SU) is maximized under the energy causality and PU collision constraints in orthogonal frequency-division multiple access (OFDMA)-based WP-CRN. Toward this, we cluster $N$ subcarriers (subchannels) into $K$ clusters (where $K \leq N$ ) and derive an optimal energy detection threshold for the SU under each traffic pattern. Using traffic features, we propose an unsupervised and nonparametric classification technique to determine the number of unique traffic patterns $K$ over all subchannels. Then, the traffic patterns are used to predict the idle/busy period statistics for subchannels, based upon which SU identifies harvest and transmit PU subchannels for energy harvesting and data transmission, respectively. We derive an optimal detection threshold based on the harvested energy such that it maximizes the expected transmission rate of SU while protecting PU from collision. We demonstrate the effectiveness of the proposed scheme in terms of rate gains under design constraints and show the optimal detection threshold under various energy arrival rates.

Cooperative jamming for energy harvesting multicast networks with an untrusted relay

Recently, there are increasing requirements of security in wireless communications, especially for the multiuser systems. In this study, the authors consider the security of two-hop cooperative multicast networks with an untrusted relay. An energy harvesting destination aided cooperative jamming framework is proposed to protect information from being intercepted by the untrusted relay. For the multiple destinations, a maximum interference jammer selection (MIJS) scheme is proposed to further improve physical layer security. Then the ergodic secrecy rate (ESR) for MIJS scheme is investigated and both the lower bound of ESR and the approximation of ESR are obtained. Finally, the effect of both the energy arrival rate and the number of destination nodes on ESR has been shown via numerical simulations. It can be revealed that as the number of destinations increases, the ESR is determined with two common behaviours regarding the multicast nature and energy arrival rate: (i) multicast nature forces the secrecy rate to decrease with the increasing number of destinations and (ii) the probability that a jammer exists increases with the increasing number of destinations but limited to one.

Modeling and understanding burst transmission for energy efficient ethernet

Recently, the energy consumption of Ethernet has become one of the hottest topics focused by both academic committee and industry, especially with the increase of the link speed from 1Gbps to 10/40Gbps nowadays or even 100/400Gbps in the near future. To save the energy consumed by the Ethernet, the Energy Efficient Ethernet (EEE) is developed and standardized by the IEEE 802.3az work group. When there is no incoming traffic, the EEE can saves 90% of its energy consumption by entering into the Low Power Idle (LPI) mode. To maximize the energy saving of Ethernet, the Burst TRansmission (BTR) mechanism, which defines a new way to utilize the LPI mode, is developed as a mechanism for EEE. Prior work theoretically shows that the BTR mechanism makes a tradeoff between the energy saving and the queuing delay. However, the traffic pattern, on which the performance of EEE greatly depends, is assumed to be deterministic in their analyses. Besides, their models made estimation for many situations. In this paper, we model EEE with the BTR mechanism and provide analytical understanding on the BTR mechanism. We propose two actual models: one focuses on the buffer size limit, the other concentrates on tolerable packet delay additionally. We draw some guidelines of parameter selection and policies design for EEE from combination of theory conclusions and simulation results. The results show that the saved energy can be constrained by link occupancy even though the buffer size is variational. The buffer full triggered wake-up policy can achieve ideal ratio of energy consumption and arrival rate within the scope of the buffer as well. However, the tolerable delay cannot be guaranteed by any policies. The buffer size is even fixed, which affects the flexibility of demanded delay for different business. The policy considering tolerable delay is supposed to be a little better than the other policy, with a little more complicated design. Thus we design an adaptive mechanism both in quantitative and qualitative: detect the load utilization, apply the buffer full triggered wake-up policy for higher load utilization link, while applying the buffer full and timeout triggered wake-up policy for the delay sensitive business and tiny arrival rate.

Policy Optimization for Content Push via Energy Harvesting Small Cells in Heterogeneous Networks

Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS (MBS) instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS. With finite battery capacity, Markov decision process based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based (POTB) policy and the energy-efficient threshold-based (EETB) policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based (GOTB) policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high.

Performance Analysis of Buffer-Aided Relaying System Based on Data and Energy Coupling Queuing Model for Cooperative Communication Networks

We focus on the performance analysis of the buffer-aided relaying system which allows data and energy packets to arrive independently and depart interactively. First, we profile the cooperative relaying system model as a data arrival and energy arrival coupling queuing model. Considering the influence of channel condition on the data departure rate, a new relay transmit protocol which permits exhausting more energy packet to send one data packet in the bad channel environment is proposed. Second, the joint data packet and energy packet handling problem is ascribed to a Coupled Processor Queuing Model which could achieve its steady state transition probability by Quasi-Birth and Death method. Third, the expressions of throughput, delay, and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the analytical results under different data arrival rate, energy arrival rate, and relaying strategy.

Green Full-Duplex Self-Backhaul and Energy Harvesting Small Cell Networks With Massive MIMO

With the dense deployment of small cell networks, the powering and backhaul problem of small cell base stations (SBSs) has attracted great attention, and energy harvesting technology and self-backhaul technology have been proposed as promising solutions. Although some excellent works have been done on energy harvesting and self-backhaul in small cell networks, most existing works do not consider them jointly. In this paper, we aim at green small cell networks by jointly achieving self-backhaul and energy harvesting. In addition, full-duplex and massive multiple-input and multiple-output technologies are also exploited to enhance the system performance. In order to improve the energy efficiency (EE) further, a novel precoding scheme is designed to eliminate both the inter-tier and multi-user interference. Based on the proposed precoding scheme, we formulate the cell association and power allocation problem as an optimization problem to optimize the system EE performance, with the energy arrival rate and remaining battery energy in SBSs involved. The formulated optimization problem implies a sleep mechanism to control the ON/OFF of SBSs, which will further reduce the energy consumption of small cell networks. In addition, to reduce the computation complexity to solve this non-convex problem, we propose to transform the original problem into a difference of convex program, which can be efficiently solved via a constrained concave convex procedure-based algorithm. Extensive simulation results are presented to justify the effectiveness of the proposed scheme with different system configurations.

Dynamic Cooperative Sensing–Access Policy for Energy-Harvesting Cognitive Radio Systems

We consider energy-harvesting cognitive radio (CR) systems with finite batteries. The energy-constrained secondary users (SUs) can be coordinated to enhance both the primary user (PU) detection and the opportunistic utilization of the PU spectrum. We employ centralized cooperative spectrum sensing, and the optimal sensors are chosen from among the SUs with varied received PU signal power levels and energy-arrival rates. The finite-horizon partially observable Markov decision process (POMDP) is applied to obtain the optimal cooperation among the SUs for sensing and access to maximize throughput with the available energy, while satisfying the PU detection constraint. The sensing-access policy decides whether to sense, the set of sensing SUs, the sensing detection threshold, and the SU that accesses the spectrum opportunities. The optimal policy achieves an optimum tradeoff between SU transmissions, preventing energy shortage and obtaining sensing information for the future gains.

DEARER: A Distance-and-Energy-Aware Routing With Energy Reservation for Energy Harvesting Wireless Sensor Networks

We consider cluster-based routing protocols for energy harvesting wireless sensor networks. Since the energy harvesting process does not match the real energy demand, sensor nodes suffer from occasional energy shortages, especially when they serve as cluster head (CH) nodes. To address this problem, we propose a cluster-based routing protocol referred to as distance-and-energy-aware routing with energy reservation (DEARER). The DEARER protocol encourages nodes with high energy-arrival rate or being close to the sink to serve as CH nodes. Also, DEARER allows non-CH nodes to reserve a portion of the harvested energy for future use. In doing so, the DEARER selects “enabler” nodes as CH nodes and provides them with more energy, thereby mitigating the energy shortage events at CH nodes. By theoretical analysis and numerical experiments, we demonstrate that the DEARER protocol outperforms direct transmission and also approaches the genie-aided routing, where CH nodes are selected based on the real-time energy information of each node.