A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations

Autor:	Gansekoele, Arwin, Balatsoukas-Stimming, Alexios, Brusse, Tom, Hoogendoorn, Mark, Bhulai, Sandjai, van der Mei, Rob
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Information Theory
Druh dokumentu:	Working Paper
Popis:	As telecommunication systems evolve to meet increasing demands, integrating deep neural networks (DNNs) has shown promise in enhancing performance. However, the trade-off between accuracy and flexibility remains challenging when replacing traditional receivers with DNNs. This paper introduces a novel probabilistic framework that allows a single DNN demapper to demap multiple QAM and APSK constellations simultaneously. We also demonstrate that our framework allows exploiting hierarchical relationships in families of constellations. The consequence is that we need fewer neural network outputs to encode the same function without an increase in Bit Error Rate (BER). Our simulation results confirm that our approach approaches the optimal demodulation error bound under an Additive White Gaussian Noise (AWGN) channel for multiple constellations. Thereby, we address multiple important issues in making DNNs flexible enough for practical use as receivers. Comment: To appear in the ICMLCN 2024 proceedings
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.09909 Zobrazit plný text záznamu View this record from Arxiv