NTF-OFDM: Robust Neural Modulation for High Mobility

Orthogonal Frequency Division Multiplexing (OFDM) is the workhorse of current 5G deployments due to its robustness in quasi-static channels and efficient spectrum use. However, in high-mobility scenarios, OFDM suffers from inter-carrier interference (ICI), and its reliance on dense pilot patterns and cyclic prefixes reduces spectral efficiency significantly. In this work, we propose NTF-OFDM (Neural Time-Frequency OFDM): a learnable modulation framework that augments traditional OFDM by incorporating neural parameterization. Instead of mapping each symbol to a fixed resource element, NTF-OFDM spreads information across the OFDM grid using a convolutional neural network modulator. This modulator is jointly optimized with a neural receiver through end-to-end training, enabling the system to adapt to time-varying channels without relying on explicit channel estimation. NTF-OFDM outperforms conventional OFDM when paired with neural receiver baselines, particularly in pilot-sparse and pilotless regimes, achieving substantial gains in BLER and goodput, particularly at high Doppler. In the pilotless setting, the neural modulator learns an implicit reference signal via constellation asymmetry, effectively enabling reliable communication without explicit overhead. These results highlight the potential of transmitter-receiver co-design for robust, resource-efficient communication in challenging channel conditions, paving the way for AI-native PHY designs in next-generation wireless systems.