Power Allocation in the Energy Harvesting Full-Duplex Gaussian Relay Channels

TL;DR

This paper develops optimal power allocation strategies for energy harvesting full-duplex Gaussian relay channels under various energy transfer scenarios, providing both optimal and suboptimal algorithms with numerical evaluations.

Contribution

It introduces a comprehensive model for energy harvesting relay channels with energy transfer, deriving optimal algorithms and conditions for different transfer cases.

Findings

Convex reformulation of the power allocation problem.

Optimal algorithms for one-way and two-way energy transfer cases.

Numerical results demonstrating algorithm performance.

Abstract

In this paper, we propose a general model to study the full-duplex non-coherent decode-and-forward Gaussian relay channel with energy harvesting (EH) nodes, called NC-EH-$\mathcal{RC}$, in three cases: $i)$ no energy transfer (ET), $ii)$ one-way ET from the source (S) to the relay (R), and $iii)$ two-way ET. We consider the problem of optimal power allocation in NC-EH-$\mathcal{RC}$ in order to maximize the total transmitted bits from S to the destination in a given time duration. General stochastic energy arrivals at S and R with known EH times and values are assumed. In NC-EH-$\mathcal{RC}$ with no ET, the complicated min-max optimization form along with its constraints make the problem intractable. It is shown that this problem can be transformed to a solvable convex form; however, convex optimization solution does not provide the structural properties of the optimal solution. Therefore, following an alternative perspective, we investigate conditions on harvesting process of S and R where we find optimal algorithmic solution. Further, we propose some suboptimal algorithms and provide some examples, in which the algorithms are optimal. Moreover, we find a class of problems for NC-EH-$\mathcal{RC}$ with one-way ET from S to R, where the optimal algorithmic solution is devised. For NC-EH-$\mathcal{RC}$ with two-way ET, we propose \emph{general} optimal algorithmic solution. Furthermore, the performance of the proposed algorithms are evaluated numerically and compared with optimal numerical convex optimization tools.