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Power Allocation in the Energy Harvesting Full-Duplex Gaussian Relay Channels
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 \(i...
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Published in: | arXiv.org 2014-11 |
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Main Authors: | , , |
Format: | Article |
Language: | English |
Subjects: | |
Online Access: | Get full text |
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Summary: | 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. |
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ISSN: | 2331-8422 |
DOI: | 10.48550/arxiv.1411.3716 |