A Novel Environmentally Robust ODDM Detection Approach Using Contrastive Learning

Abstract

Deep learning (DL) demonstrates tremendous potential in high-mobility communication systems, especially from the perspective of signal detection. However, most existing DL-based detection methods are data/environment specific and the re-training process is resource intensive. To address these shortcomings, we propose a contrastive learning-based environmentally robust signal detection approach in orthogonal delay-Doppler division multiplexing (CL-ODDM) to achieve fast convergence, high accuracy and strong robustness to variations in wireless environments. Specifically, unlike conventional methods which explore only positive samples in the dataset for detection, in this work, we propose to leverage contrastive learning to fully exploit both positive and negative samples in the training dataset. This enables us to extract more comprehensive features of signals, which can accelerate convergence, improve detection accuracy, and enhance the generalized ability of our CL-ODDM. Moreover, we creatively employ a convolutional neural network and recurrent encoder-decoder (CREN) to represent the underlying properties of ODDM signals and extract high-quality features. To further improve environmental robustness, we propose novel training strategies, i.e., data augmentation (DA) and adaptive updating scheme (AUS). The proposed DA method is expected to increase the diversity of the dataset and represent more effective signal features. The designed AUS leverages transfer learning to adapt partial layers of CREN to make our CL-ODDM suitable for various wireless environments. Numerous simulation results validate that the proposed CL-ODDM significantly outperforms state-of-the-art related works, in terms of detection accuracy, environmental robustness and convergence rate.