Layered Coding for Energy Harvesting Communication Without CSIT

TL;DR

This paper investigates layered coding strategies for energy harvesting communication systems without channel state information at the transmitter, optimizing average data rates under resource constraints.

Contribution

It introduces layered coding approaches for energy harvesting channels without CSIT, including strategies for finite batteries and power variation knowledge, with new algorithms and comparisons.

Findings

Layered coding improves data rates in energy harvesting channels.

Proposed algorithms outperform greedy policies in simulations.

Comparison shows benefits of layered coding over traditional methods.

Abstract

Due to stringent constraints on resources, it may be infeasible to acquire the current channel state information at the transmitter in energy harvesting communication systems. In this paper, we optimize an energy harvesting transmitter, communicating over a slow fading channel, using layered coding. The transmitter has access to the channel statistics, but does not know the exact channel state. In layered coding, the codewords are first designed for each of the channel states at different rates, and then the codewords are either time-multiplexed or superimposed before the transmission, leading to two transmission strategies. The receiver then decodes the information adaptively based on the realized channel state. The transmitter is equipped with a finite-capacity battery having non-zero internal resistance. In each of the transmission strategies, we first formulate and study an average rate maximization problem with non-causal knowledge of the harvested power variations. Further, assuming statistical knowledge and causal information of the harvested power variations, we propose a sub-optimal algorithm, and compare with the stochastic dynamic programming based solution and a greedy policy.