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.

翻译：随着电信系统为满足日益增长的需求而演进，集成深度神经网络在提升性能方面展现出潜力。然而，用深度神经网络替代传统接收机时，精度与灵活性之间的权衡仍具挑战性。本文提出一种新颖的概率框架，使得单个深度神经网络解映射器能同时解映射多种QAM与APSK星座。我们还证明该框架可利用星座族内部的层级关系，从而在误码率不增加的前提下，用更少的神经网络输出编码相同函数。仿真结果证实，本方法在加性高斯白噪声信道下对于多种星座均能逼近最优解调误差界。由此，我们解决了使深度神经网络具备足够灵活性以用于实际接收机的多个关键问题。