Dor Shaviv

Universally near-optimal online power control for energy harvesting nodes

We consider online power control for an energy harvesting system with random i.i.d. energy arrivals and a finite size battery. We propose a simple online power control policy for this channel that requires minimal information regarding the distribution of the energy arrivals and prove that it is universally near-optimal for all parameter values. In particular, the policy depends on the distribution of the energy arrival process only through its mean and achieves the optimal long-term average throughput of the channel within a constant gap. Existing heuristics for online power control fail to achieve such universal performance. This result also allows us to obtain a simple constant-gap approximation for the long-term average throughput of the system, which sheds some light on the qualitative behavior of the throughput, namely how it depends on the distribution of the energy arrivals and the size of the battery.

Capacity of the AWGN channel with random battery recharges

We consider communication over the AWGN channel with a transmitter whose battery is recharged with RF energy transfer at random times known to the receiver. We assume that the recharging process is i.i.d. Bernoulli. We characterize the capacity of this channel as the limit of an n-letter maximum mutual information rate under both causal and noncausal transmitter knowledge of the battery recharges. With noncausal knowledge, it is possible to explicitly identify the maximizing input distribution, which we use to demonstrate that the capacity with noncausal knowledge of the battery recharges is strictly larger than that with causal knowledge. We then proceed to derive explicit upper and lower bounds on the capacity, which are within 1.05 bits/s/Hz of each other for all parameter values.

Capacity of the energy harvesting channel with a finite battery

We consider an energy harvesting channel, in which the transmitter is powered by an exogenous stochastic energy harvesting process Et, such that 0 ≤ Et ≤ E̅, which can be stored in a battery of finite size B̅. We provide a simple and insightful formula for the approximate capacity of this channel with bounded guarantee on the approximation gap, independent of system parameters. This approximate characterization of the capacity identifies two qualitatively different operating regimes for this channel: in the large battery regime, when B̅ ≥ E̅, the capacity is approximately equal to that of an additive white Gaussian noise channel with an average power constraint equal to the average energy harvesting rate, i.e., it depends only on the mean of Et and is (almost) independent of the distribution of Et and the exact value of B̅. In particular, this suggests that a battery size B̅ ≈ E̅ is approximately sufficient to extract the infinite battery capacity of the system. In the small battery regime, when B̅ <; E̅, we clarify the dependence of the capacity on the distribution of Et and the value of B̅. There are three steps to proving this result, which can be of interest in their own right: 1) we characterize the capacity of this channel as an n-letter mutual information rate under various assumptions on the availability of energy arrival information: causal and noncausal knowledge of the energy arrivals at the transmitter with and without knowledge at the receiver; 2) we characterize the approximately optimal online power control policy that maximizes the long-term average throughput of the system; and 3) we show that the information-theoretic capacity of this channel is equal, within a constant gap, to its long-term average throughput. This last result provides a connection between the information- and communication-theoretic formulations of the energy harvesting communication problem that have been so far studied in isolation.

Can feedback increase the capacity of the energy harvesting channel

We investigate if feedback can increase the capacity of an energy harvesting communication channel where a transmitter powered by an exogenous energy arrival process and equipped with a finite battery communicates to a receiver over a memoryless channel. For a simple special case where the energy arrival process is deterministic and the channel is a BEC, we explicitly compute the feed-forward and feedback capacities and show that feedback can strictly increase the capacity of this channel. Building on this example, we also show that feedback can increase the capacity when the energy arrivals are i.i.d. known noncausally at the transmitter and the receiver.

Capacity of remotely powered communication

Motivated by the recent developments in wireless power transfer, we study communication with a remotely powered transmitter. We propose an information-theoretic model where a charger can dynamically decide on how much power to transfer to the transmitter based on its side information regarding the communication, while the transmitter needs to dynamically adapt its coding strategy to its instantaneous energy state, which in turn depends on the actions previously taken by the charger. We characterize the capacity as an

On the Multiple-Access Channel with Common Rate-Limited Feedback

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Capacity of Remotely Powered Communication

-letter mutual information rate under various levels of side information available at the charger. When the charger is finely tunable to different energy levels, referred to as a “precision charger,” we show that these expressions reduce to single-letter form and there is a simple and intuitive joint charging and coding scheme achieving capacity. The precision charger scenario is motivated by the observation that in practice the transferred energy can be controlled by simply changing the amplitude of the beamformed signal. When the charger does not have sufficient precision, for example, when it is restricted to use a few discrete energy levels, we show that the computation of the

Capacity of the energy harvesting Gaussian MAC

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On the Multiple-Access Channel With Common Rate-Limited Feedback

-letter capacity can be cast as a Markov decision process if the channel is noiseless. This allows us to numerically compute the capacity for specific cases and obtain insights on the corresponding optimal policy, or even to obtain closed-form analytical solutions by solving the corresponding Bellman equations, as we demonstrate through examples. Our findings provide some surprising insights on how side information at the charger can be used to increase the overall capacity of the system.

Approximately optimal policies for a class of Markov decision problems with applications to energy harvesting

This work studies the multiple access channel (MAC) with rate-limited feedback. The channel output is encoded into one stream of bits, which is provided causally to the two users at the channel input. An achievable rate region for this setup is derived, based on superposition of information, block Markov coding, and coding with various degrees of side information, for the feedback link. The suggested region coincides with the Cover-Leung inner bound for large feedback rates.