Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network

Jiusi Zhou, Shuping Dang, Basem Shihada, Mohamed-Slim Alouini

Research output: Contribution to journalArticlepeer-review

Abstract

In this letter, we propose a power allocation scheme for relayed orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. The proposed power allocation scheme replies on artificial neural network (ANN) and deep learning to allocate transmit power among various subcarriers at the source and relay nodes. The objective of the power allocation scheme is to minimize the overall transmit power under a set of constraints. Without loss of generality, we assume all subcarriers at source and relay nodes are independently distributed with different statistical distribution parameters. The relay node adopts the fixed-gain amplify-and-forward (FG AF) relaying protocol. We employ the adaptive moment estimation method (Adam) to implement back-propagation learning and simulate the proposed power allocation scheme. The analytical and simulation results show that the proposed power allocation scheme is able to provide comparable performance as the optimal solution but with lower complexity.
Original languageEnglish (US)
Pages (from-to)1-1
Number of pages1
JournalIEEE Wireless Communications Letters
DOIs
StatePublished - 2020

Fingerprint

Dive into the research topics of 'Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network'. Together they form a unique fingerprint.

Cite this