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

TL;DR

This paper presents a probabilistic deep neural network framework capable of simultaneously demapping multiple QAM and APSK constellations, improving flexibility and efficiency in communication receivers while maintaining near-optimal error performance.

Contribution

A novel probabilistic framework enabling a single DNN to demap multiple constellation types simultaneously, leveraging hierarchical relationships to reduce network complexity.

Findings

Approaches the optimal demodulation error bound under AWGN.

Reduces neural network output complexity without increasing BER.

Demonstrates practical flexibility of DNN-based receivers.

Abstract

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.