AI/ML Optimized High-Order Modulations
Pranav Madadi, Joonyoung Cho, Jianzhong Charlie Zhang, Daoud Burghal · 2023
We propose machine learning (ML) based optimization methods and new high order modulations for reliable and high-capacity communications. The widely adopted square quadrature amplitude modulations (QAM) fundamentally exhibit a shaping loss of up to 1.53 dB to the Shannon capacity bound. The proposed modulations obtained through the ML based optimization outperform the square QAMs and other state of-the-art ones by about 1.2 dB and 0.3 dB, respectively, for 1024-ary modulation with LDPC coding. We construct the neural network architecture and training methods to reflect the desired properties of well-performing modulations. This significantly helps in the training convergence of the ML models to a desired optimal state and leads to the modulation constellation and bit to-symbol mapping that reduces the shaping loss to the Shannon capacity bound to a large extent. Moreover, the ML methods enable the development of new optimal modulations for a wide range of target SNR and modulation orders.