Learning During Detection: Continual Learning for Neural OFDM Receivers via DMRS
This addresses the challenge of maintaining neural receiver performance in dynamic communication environments, though it is incremental as it builds on existing neural receiver methods with novel pilot designs and architectures.
The paper tackled the problem of neural OFDM receivers needing periodic retraining due to rapid channel changes by proposing a zero-overhead online continual learning framework that uses demodulation reference signals for simultaneous demodulation and adaptation, achieving effective tracking of channel variations without additional overhead or service interruption.
Deep neural networks (DNNs) have been increasingly explored for receiver design because they can handle complex environments without relying on explicit channel models. Nevertheless, because communication channels change rapidly, their distributions can shift over time, often making periodic retraining necessary. This paper proposes a zero-overhead online and continual learning framework for orthogonal frequency-division multiplexing (OFDM) neural receivers that directly detect the soft bits of received signals. Unlike conventional fine-tuning methods that rely on dedicated training intervals or full resource grids, our approach leverages existing demodulation reference signals (DMRS) to simultaneously enable signal demodulation and model adaptation. We introduce three pilot designs: fully randomized, hybrid, and additional pilots that flexibly support joint demodulation and learning. To accommodate these pilot designs, we develop two receiver architectures: (i) a parallel design that separates inference and fine-tuning for uninterrupted operation, and (ii) a forward-pass reusing design that reduces computational complexity. Simulation results show that the proposed method effectively tracks both slow and fast channel distribution variations without additional overhead, service interruption, or catastrophic performance degradation under distribution shift.