2,894 research outputs found
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 it achieves the optimal long-term average throughput of the channel within
both constant additive and multiplicative gaps. Existing heuristics for online
power control fail to achieve such universal performance. This result also
allows us to approximate the long-term average throughput of the system with a
simple formula, 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.Comment: the proposed scheme is shown to be optimal both within constant
additive and multiplicative gaps; submitted to Journal on Selected Areas in
Communications - Series on Green Communications and Networking (Issue 3);
revised following reviewers' comment
Energy Harvesting Networks with General Utility Functions: Near Optimal Online Policies
We consider online scheduling policies for single-user energy harvesting
communication systems, where the goal is to characterize online policies that
maximize the long term average utility, for some general concave and
monotonically increasing utility function. In our setting, the transmitter
relies on energy harvested from nature to send its messages to the receiver,
and is equipped with a finite-sized battery to store its energy. Energy packets
are independent and identically distributed (i.i.d.) over time slots, and are
revealed causally to the transmitter. Only the average arrival rate is known a
priori. We first characterize the optimal solution for the case of Bernoulli
arrivals. Then, for general i.i.d. arrivals, we first show that fixed fraction
policies [Shaviv-Ozgur] are within a constant multiplicative gap from the
optimal solution for all energy arrivals and battery sizes. We then derive a
set of sufficient conditions on the utility function to guarantee that fixed
fraction policies are within a constant additive gap as well from the optimal
solution.Comment: To appear in the 2017 IEEE International Symposium on Information
Theory. arXiv admin note: text overlap with arXiv:1705.1030
Learning Aided Optimization for Energy Harvesting Devices with Outdated State Information
This paper considers utility optimal power control for energy harvesting
wireless devices with a finite capacity battery. The distribution information
of the underlying wireless environment and harvestable energy is unknown and
only outdated system state information is known at the device controller. This
scenario shares similarity with Lyapunov opportunistic optimization and online
learning but is different from both. By a novel combination of Zinkevich's
online gradient learning technique and the drift-plus-penalty technique from
Lyapunov opportunistic optimization, this paper proposes a learning-aided
algorithm that achieves utility within of the optimal, for any
desired , by using a battery with an capacity. The
proposed algorithm has low complexity and makes power investment decisions
based on system history, without requiring knowledge of the system state or its
probability distribution.Comment: This version extends v1 (our INFOCOM 2018 paper): (1) add a new
section (Section V) to study the case where utility functions are non-i.i.d.
arbitrarily varying (2) add more simulation experiments. The current version
is published in IEEE/ACM Transactions on Networkin
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