We consider a binary energy harvesting communication channel with a finite-sized battery at the transmitter. In this model, the channel input is constrained by the available energy at each channel use, which is driven by an external energy harvesting process, the size of the battery, and the previous channel inputs. We consider an abstraction where energy is harvested in binary units and stored in a battery with the capacity of a single unit, and the channel inputs are binary. Viewing the available energy in the battery as a state, this is a state-dependent channel with input-dependent states, memory in the states, and causal state information available at the transmitter only. We find an equivalent representation for this channel based on the timings of the symbols, and determine the capacity of the resulting equivalent timing channel via an auxiliary random variable. We present achievable rates based on certain selections of this auxiliary random variable, which resemble lattice coding for the timing channel. We develop upper bounds for the capacity by using a genie-aided method, and also by quantifying the leakage of the state information to the receiver. We show that the proposed achievable rates are asymptotically capacity achieving for small energy harvesting rates. We extend the results to the case of ternary channel inputs. We numerically observe that our achievable rates are notably close to the upper bounds, and outperform basic Shannon strategies that only consider instantaneous battery states, for all parameter values.

A noiseless binary energy harvesting channel with on-off fading is considered. When causal fading state information is available at the transmitter only, an equivalent timing channel with additive geometric noise and noise information known at the transmitter is obtained. In this channel, the transmitter's strategy is a stopping rule with respect to the channel fade levels given the message and the additive noise. Next, capacity when energy arrival information is available at the receiver and capacity when both energy arrival and fading information are available at the receiver are obtained. Additionally, several achievable schemes are proposed and evaluated.

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.

In this paper, we study the two-way relay channel with energy harvesting nodes. In particular, we find transmission policies that maximize the sum-throughput for two-way relay channels when the relay does not employ a data buffer. The relay can perform decode-and-forward, compress-and-forward, compute-and-forward, or amplify-and-forward relaying. Furthermore, we consider throughput improvement by dynamically choosing relaying strategies, resulting in hybrid relaying strategies. We show that an iterative generalized directional water-filling algorithm solves the offline throughput maximization problem, with the achievable sum-rate from an individual or hybrid relaying scheme. In addition to the optimum offline policy, we obtain the optimum online policy via dynamic programming. We provide numerical results for each relaying scheme to support the analytic findings, pointing out to the advantage of adapting the instantaneous relaying strategy to the available harvested energy.

This paper considers multiterminal networks with energy harvesting transmitter nodes that are also capable of wirelessly transferring energy to or receiving energy from other nodes in the network. In particular, the jointly optimal transmit power and energy transfer policies that maximize sum-throughput for the two-way, two-hop, and multiple access channels are identified. It is shown for nodes with infinite-sized batteries that delaying energy transfers until energy is needed immediately at the receiving node is sum-throughput optimal. Focusing on such procrastinating policies without loss of optimality, the stated joint optimization problem can be decomposed into energy transfer and consumed energy allocation problems which are solved in tandem. This decomposition is shown to hold for the finite-sized battery case as well, using partially procrastinating policies that avoid battery overflows. It is observed that for the two-hop channel, the proposed algorithm has a two fluid water-filling interpretation, and for the multiple access channel, it reduces to a single transmitter problem with aggregate energy arrivals. Numerical results demonstrate the throughput improvement with bi-directional energy cooperation over no cooperation and uni-directional cooperation.

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms.

In this paper, we consider the energy allocation problem for energy harvesting and energy cooperating nodes with finite-sized batteries. In particular, we solve the sum-throughput maximization problem in a two-way channel with energy harvesting nodes that can also transfer energy to one another. To do so, we non-trivially extend a class of policies which originally rely on an infinite-sized battery to be optimal, to the finite battery case. We observe that when we partition transferred energy into immediately used and stored components, an optimal policy has a non-zero stored component only when the battery of the transferring user is full. This enables the decomposition of the sum-throughput maximization problem into separate energy transfer and power allocation problems. Utilizing properties of this optimal class of policies, we solve the power allocation problem using a two dimensional directional water-filling algorithm with restricted transfers, where energy transfers only take place at full battery instances. Numerical results demonstrate that energy cooperation notably improves sum-throughput as one node gets energy deprived.

We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and its battery state information available at the transmitter and the receiver. This capacity is an upper bound for the problem where side information is available only at the transmitter. Since channel output feedback does not increase the capacity in this case, we equivalently study the resulting finite-state Markov channel with feedback. We express the capacity in terms of directed information. Additionally, we provide sufficient conditions under which the capacity expression is further simplified to include the stationary distribution of the battery state. We also obtain a single-letter expression for the capacity with battery state information at both sides and an infinite-sized battery. Lastly, we consider achievable schemes when side information is available only at the transmitter for the case of an arbitrary finite-sized battery. We numerically evaluate the capacity and achievable rates with and without receiver side information.

We consider a binary energy harvesting channel (BEHC) where the encoder has unit energy storage capacity. We first show that an encoding scheme based on block indexing is asymptotically optimal for small energy harvesting rates. We then present a novel upper bounding technique, which upper bounds the rate by lower-bounding the rate of information leakage to the receiver regarding the energy harvesting process. Finally, we propose a timing based hybrid encoding scheme that achieves rates within 0.03 bits/channel use of the upper bound; hence determining the capacity to within 0.03 bits/channel use.

We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and the energy arrival information available at the receiver as well as the transmitter. We obtain an n-letter capacity expression and prove that the capacity is achieved by an encoding scheme that depends only on the current battery state. Moreover, the capacity is invariant to the non-causal knowledge of energy arrivals. Finally, we show that the capacity expression is equivalently the maximum directed mutual information and that the channel output feedback does not increase the capacity in this case. We obtain upper and lower bounds on the capacity and numerically evaluate them for comparison.

In this paper, we consider a binary energy harvesting transmitter that wishes to control the amount of side information the receiver can obtain about its energy harvests. Specifically, we study state amplification and state masking, which define the maximum and minimum amount of state information conveyed to the receiver for a given message rate, respectively. For an independent and identically distributed energy harvesting process, we first find the amplification and masking regions for a transmitter without a battery and a transmitter with an infinite battery. Next, we find inner bounds for these regions for a unit-sized battery at the transmitter using two different encoding schemes, using instantaneous Shannon strategies and using a scheme based on the equivalent timing channel introduced in our previous work. We observe that the former provides better state amplification, while the latter provides better state masking.

We consider the capacity of an energy harvesting communication channel with a finite-sized battery. As an abstraction of this problem, we consider a system where energy arrives at the encoder in multiples of a fixed quantity, and the physical layer is modeled accordingly as a finite discrete alphabet channel based on this fixed quantity. Further, for tractability, we consider the case of binary energy arrivals into a unit-capacity battery over a noiseless binary channel. Viewing the available energy as state, this is a state-dependent channel with causal state information available only at the transmitter. Further, the state is correlated over time and the channel inputs modify the future states. We show that this channel is equivalent to an additive geometric-noise timing channel with causal information of the noise available at the transmitter. We provide a single-letter capacity expression involving an auxiliary random variable, and evaluate this expression with certain auxiliary random variable selection, which resembles noise concentration and lattice-type coding in the timing channel. We evaluate the achievable rates by the proposed auxiliary selection and extend our results to noiseless ternary channels.

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.

This work considers a Gaussian multiple access channel with two energy harvesting transmitters with lossy energy storage. The power allocation policy maximizing the average weighted sum rate given the energy harvesting profiles is found. In particular, it is shown that the optimal policy has a double-threshold structure on each of the transmit powers, while the two transmit powers interact through the multivariate achievable rate function which determines the thresholds. For the special case of sum rate maximization in a Gaussian MAC channel, it is shown that the thresholds apply to the sum power, and the optimal policy consists of three thresholds, rather than four, which enables the user with a more efficient battery to be given priority in energy storage.

Wireless networks with energy harvesting battery equipped nodes are quickly emerging as a viable option for future wireless networks with extended lifetime. Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for. In particular, unlike wireless networks considered to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies. In this work, such transmission policies for rechargeable nodes are considered, and optimum solutions for two related problems are identified. Specifically, the transmission policy that maximizes the short term throughput, i.e., the amount of data transmitted in a finite time horizon is found. In addition, the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data is demonstrated, which leads to the solution of the latter as well. The optimum transmission policies are identified under the constraints on energy causality, i.e., energy replenishment process, as well as the energy storage, i.e., battery capacity. For battery replenishment, a model with discrete packets of energy arrivals is considered. The necessary conditions that the throughput-optimal allocation satisfies are derived, and then the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time is given. Numerical results are presented to confirm the analytical findings.

Deploying energy harvesting nodes can significantly extend the lifetime of a battery powered wireless network. The nature of these systems calls for specific design principles to efficiently utilize the dynamic energy resources. In contrast to the conventional wireless networks, transmission policies for these systems should take into account the recharge process of the node as well as its battery capacity. In this work, we focus on the problem of finding the optimal transmission policy to maximize the short-term throughput of an energy harvesting transmitter node with power control and a limited energy storage capacity. The transmission policy in this problem is strictly constrained with both the energy arrival process and the energy storage capacity of the node. The analysis of the problem is founded on the increasing concave nature of the power-rate relationship information theoretic findings suggest. A discrete model with packets of energy arrivals is considered for energy replenishment, and an algorithm to yield the short-term throughout maximizing policy is sought. The necessary conditions such a policy should satisfy are identified and an algorithm to yield the unique policy that satisfies these conditions is presented. The performance of this algorithm is then observed through numerical results.

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 the transmission policy of an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. In particular, we identify the optimal offline transmission policies that maximize the number of bits delivered by a deadline, and minimize the transmission completion time of the communication session. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions as well as energy storage capacity and causality. We solve the throughput maximization problem for the fading channel using the directional water-filling algorithm, which simultaneously adapts to the energy harvested as well as the channel variations in time. We then solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart.

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.