A Machine Learning Approach for Simultaneous Demapping of QAM and APSK Constellations
This addresses the challenge of flexibility in DNN-based receivers for telecommunication systems, representing an incremental improvement by enhancing existing methods.
The paper tackles the problem of making deep neural network (DNN) receivers more flexible for telecommunications by introducing a probabilistic framework that enables a single DNN to demap multiple QAM and APSK constellations simultaneously, achieving performance near the optimal demodulation error bound under AWGN channels.
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.