Burak Varan

Optimum transmission policies for energy harvesting two-way relay channels

In this paper, two-way relay channels with energy harvesting nodes are considered. In particular, short-term sum-rate maximization problem is solved for half-duplex and full-duplex channels under any relaying strategy. Instantaneous rates achieved with energy constraints are evaluated and compared for different relaying strategies, namely amplify-and-forward, decode-and-forward, compress-and-forward and compute-and-forward. A generalized iterative directional water-filling algorithm is shown to solve the sum-rate maximization problem for an arbitrary jointly concave achievable sum-rate, which is constructed by concavifying the rate achievable by a relaying scheme. Observing that optimal relaying scheme depends on the power vectors, a hybrid strategy switching between relaying schemes is proposed, and numerical results demonstrating the advantage of hybrid strategies in an energy harvesting setting are presented.

Energy harvesting two-way half-duplex relay channel with decode-and-forward relaying: Optimum power policies

In this paper, a half-duplex two-way relay channel with energy harvesting nodes is considered. In particular, short-term throughput maximization problems are solved using a decode-and-forward relay. Necessary properties of the optimal transmission policy are derived to gain insights into the optimal solution. Then, a subgradient descent algorithm is used to find the optimal policy. It is observed through simulations that energy-deficient nodes act as bottlenecks on the achieved throughput. The achieved average throughput is observed to be close to the upper bound when nodes have no energy intermittency, and significantly higher than the throughput achieved by naïve policies.

Throughput Maximization for Two-Way Relay Channels With Energy Harvesting Nodes: The Impact of Relaying Strategies

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.

Two-hop networks with energy harvesting: The (non-)impact of buffer size

In this paper, a two-hop channel is considered with energy harvesting transmitter nodes. In particular, the offline throughput maximization problem is solved for a constant power relay, and a relay with one energy arrival, in both cases assuming a finite buffer is available at the relay for temporarily storing data received from the source. The focus is on assessing the impact of this data buffer at the relay on optimal transmission policies. The solution is found indirectly, by first assuming that the relay has an infinite size buffer, and then proving that an optimal policy exists that does not require any data buffer at the relay, thus solving the problem regardless of the data buffer size at the relay. Numerical results demonstrate that the proposed solution performs significantly better than naïve policies, and a constant relay rate limits the average throughput as the peak energy harvest rate for the source increases.

The energy harvesting two-way decode-and-forward relay channel with stochastic data arrivals

In this paper, a two-way relay channel is considered with energy harvesting nodes and stochastic data arrivals at the source nodes. The batteries and data buffers at all nodes are of finite storage capability. The sum throughput maximization problem for this set up is shown to be a convex optimization problem, and the optimal offline policy is found. Numerical results are presented to demonstrate the optimal policy for different channel setups, and its performance.

Energy harvesting communications with continuous energy arrivals

This work considers an energy harvesting transmitter that gathers a continuous flow of energy from intermittent sources, thus relaxing the modeling assumption of discrete amounts of harvested energy present in all previous work on energy harvesting communications. Tools from convex analysis are utilized to describe the optimal transmission policy as the boundary of a properly defined region based on the energy profile. The results are extended to include models where the transmitter has a finite capacity battery with various imperfections, as well as those that incorporate a processing cost (circuit power) at the transmitter whenever it is in operation.

Delay Constrained Energy Harvesting Networks with Limited Energy and Data Storage

This paper studies energy harvesting transmitters in the single user channel, the two-way channel, and the two-way relay channel with block fading. Each transmitter is equipped with a finite battery to store the harvested energy, and a finite buffer to store the data that arrive during the communication session. We consider delay sensitive applications and maximize throughput while enabling timely delivery of data with delay constraints. We show that the resulting delay limited throughput maximization problem can be solved using alternating maximization of two decoupled problems termed the energy scheduling problem and the data scheduling problem. We solve the energy scheduling problem using a modified directional waterfilling algorithm with right permeable taps, water pumps, and overflow bins and the data scheduling problem with forward induction. Additionally, we identify the online optimum policy for throughput maximization. We provide numerical results to verify our analytical findings and to demonstrate the impact of the finite data buffer capacity and the delay requirements on the throughput. We observe that larger buffer sizes become useful for more lenient delay requirements, and a data buffer size that is comparable to the throughput within one time slot accounts for the majority of the increase in throughput.

Optimal strategies for targeted influence in signed networks

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.

Energy harvesting two-way communications with limited energy and data storage

In this paper, we study two-way communication scenarios with energy harvesting. In particular, we consider the two-way and two-way relay channels with finite data storage. We solve the throughput maximization problem with finite batteries and finite data buffers. This entails iteratively solving an energy problem, which distributes the available energy over the course of the communication session, and a data problem, which schedules how much data to send at each node over the course of the communication session. We provide a directional waterfilling interpretation to the energy problem with the addition of water pumps and overflow protection bins. The data problem turns out to be a linear program which we solve by forward induction. We provide numerical results demonstrating the impact of the battery and buffer sizes on the achieved throughput. We observe, for communication scenarios of interest, that a relatively modest size of data storage is sufficient to harness the performance benefits of data buffering, i.e., to achieve the throughput values without buffer size limitations.

Throughput maximizing games in the two-hop relay channel with energy cooperation

In this paper, we study a two-hop network where the source and the relay have data that the destination wishes to receive. The source node is not directly connected to the destination; it can send its data only via the relay. The relay node, on the other hand, does not have an external source of energy, and needs to perform RF energy harvesting from the source to send its and the sources data. Both nodes wish to send as much of their data to the destination as possible. For this setup, we first formulate a noncooperative game and improve upon its equilibrium by using a pricing scheme. Next, we model the communication setup as a Stackelberg game with the relay node as the leader and the source node as the follower of the game. We analyze the resulting equilibrium and interpret how the leader of the game chooses its strategy in order to influence the followers decision. We provide numerical examples which compare the payoffs achieved by these equilibria. We investigate the impact of the model parameters on the decisions of the two players and the achieved payoffs. We observe that at the Stackelberg equilibrium, the leader of the game can manipulate the follower in order to achieve a higher payoff than it would at the social optimum.