Adaptive Neural Network-based OFDM Receivers

TL;DR

This paper introduces a method for neural network-based OFDM receivers to adapt in real-time to changing channel conditions and unforeseen interferences by retraining with recovered labels, enhancing robustness and performance.

Contribution

It presents a novel online adaptation approach for NN-based OFDM receivers that does not require additional pilots, enabling effective mitigation of diverse channel variations and disturbances.

Findings

Significant performance gains in static channel scenarios.

Effective adaptation to out-of-specification conditions.

Improved interference mitigation capabilities.

Abstract

We propose and examine the idea of continuously adapting state-of-the-art neural network (NN)-based orthogonal frequency division multiplex (OFDM) receivers to current channel conditions. This online adaptation via retraining is mainly motivated by two reasons: First, receiver design typically focuses on the universal optimal performance for a wide range of possible channel realizations. However, in actual applications and within short time intervals, only a subset of these channel parameters is likely to occur, as macro parameters, e.g., the maximum channel delay, can assumed to be static. Second, in-the-field alterations like temporal interferences or other conditions out of the originally intended specifications can occur on a practical (real-world) transmission. While conventional (filter-based) systems would require reconfiguration or additional signal processing to cope with these unforeseen conditions, NN-based receivers can learn to mitigate previously unseen effects even after their deployment. For this, we showcase on-the-fly adaption to current channel conditions and temporal alterations solely based on recovered labels from an outer forward error correction (FEC) code without any additional piloting overhead. To underline the flexibility of the proposed adaptive training, we showcase substantial gains for scenarios with static channel macro parameters, for out-of-specification usage and for interference compensation.