Environment-robust signal detection for OTFS systems using deep learning

Deep learning (DL)-based signal detection techniques have demonstrated significantly superior performance than the conventional methods in orthogonal time frequency space (OTFS) systems. Despite the effectiveness, existing methods using DL techniques are environment-specific. For instance, a detection model trained in one environment may become ineffective if environmental changes occur, e.g., user scheduling, inter-user interference and network scheduling. A re-training process is required to refine the model using numerous samples from the new/unseen environment, which is not always accessible in practice. To address the above concern, in this work, we propose an environment-robust approach to detect OTFS signals, by leveraging the property of the matching network (MatNet), referred as to OTFS-MatNet. Specifically, we propose to employ two functional blocks of MatNet to automatically capture generalized features shared among seen environments and the potential new environment. We also develop a novel loss function and a two-step training strategy to improve the generalized ability and detection accuracy. Therefore, the proposed OTFS-MatNet can realize accurate detection with a limited number of training samples, i.e., one sample from the new environment and the dataset from one seen environment. Numerous simulation results demonstrate that the developed OTFS-MatNet is significantly superior to state-of-the-art OTFS detection methods, in terms of improving detection accuracy and reducing the required number of training samples.