Reinforcement Learning Empowered Digital Pre-Distortion in Non-Differentiable Channel

This work proposes a novel digital pre-distortion technique based on deep reinforcement learning to mitigate instantaneous nonlinearities in communication systems stemming from nonideal electrical components. Comparative simulation results demonstrate its significant performance gains over conventional methods, including Volterra series and look-up tables, in terms of bit error rate reduction and effective SNR enhancement.

Authors

Institutions

Publication Details

Published
2025-05-20
DOI
https://doi.org/10.1109/pn66844.2025.11097158
Citations
1
Primary Topic
PAPR reduction in OFDM
Type
article
Field-Weighted Citation Impact
0.73
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Reinforcement Learning Empowered Digital Pre-Distortion in Non-Differentiable Channel

Ming Zeng, Leslie A. Rusch, Arash Rabiepoor
1 citations
PAPR reduction in OFDM
0.73
article

Reinforcement Learning Empowered Digital Pre-Distortion in Non-Differentiable Channel

Ming Zeng, Leslie A. Rusch, Arash Rabiepoor
article en
1 citations

Abstract

This work proposes a novel digital pre-distortion technique based on deep reinforcement learning to mitigate instantaneous nonlinearities in communication systems stemming from nonideal electrical components. Comparative simulation results demonstrate its significant performance gains over conventional methods, including Volterra series and look-up tables, in terms of bit error rate reduction and effective SNR enhancement.

Instituto de Óptica "Daza de Valdés" (ES)
Reduced inequalities
Openalex Percentile: Top 25%
PAPR reduction in OFDM
0.73
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.