Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network
Type
ArticleKAUST Department
Communication Theory LabComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering
Electrical Engineering Program
Networks Laboratory (NetLab)
Date
2020-10-16Preprint Posting Date
2020-10-24Online Publication Date
2020-10-16Print Publication Date
2021-02Permanent link to this record
http://hdl.handle.net/10754/665614
Metadata
Show full item recordAbstract
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.Citation
Zhou, J., Dang, S., Shihada, B., & Alouini, M.-S. (2020). Power Allocation for Relayed OFDM with Index Modulation Assisted by Artificial Neural Network. IEEE Wireless Communications Letters, 1–1. doi:10.1109/lwc.2020.3031638arXiv
2010.12959Additional Links
https://ieeexplore.ieee.org/document/9226618/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9226618
ae974a485f413a2113503eed53cd6c53
10.1109/LWC.2020.3031638