AI/ML-Based Asymmetric Modulation Constellations and Pilotless Communications

We propose a machine learning (ML) based end-to-end framework for pilotless communications that consists of two key components. The first component is an asymmetric modulation constellation that enables pilotless communications under channel impairments. The second component is a neural network (NN) receiver featuring an architecture that has a core of several serially-connected ResNet-like blocks. The transmitter only sends data symbols (without any pilots), and the NN receiver enables pilotless communications by using the received data symbols from the asymmetric constellation to perform implicit channel estimation/compensation and generate log-likelihood ratios (LLRs) for the bits comprising the data symbols. The combination of the asymmetric modulation constellation and the NN receiver achieves similar or superior performance to a traditional zero-forcing (ZF) receiver that relies on pilot symbols for channel estimation for 64-ary and 256-ary modulations for channels with limited time and frequency selectivity.