🤖 AI Summary
Offline-trained neural networks for channel estimation suffer from poor generalization, reliance on prior channel knowledge, and difficulty adapting to unknown time-varying channels. To address this, we propose a network-agnostic synthetic data design principle that models channel statistics and delay-spread dynamics to construct a robust training set covering the boundaries of the channel distribution. Our approach requires neither online fine-tuning nor real-time channel feedback, significantly enhancing model generalization to unseen channels. Experiments demonstrate that the proposed method satisfies prescribed robustness requirements in terms of mean squared error under both fixed and variable delay-spread scenarios. Moreover, it achieves low latency and low computational overhead, making it suitable for deployment in resource-constrained real-time wireless communication systems.
📝 Abstract
Channel estimation is crucial in cognitive communications, as it enables intelligent spectrum sensing and adaptive transmission by providing accurate information about the current channel state. However, in many papers neural networks are frequently tested by training and testing on one example channel or similar channels. This is because data-driven methods often degrade on new data which they are not trained on, as they cannot extrapolate their training knowledge. This is despite the fact physical channels are often assumed to be time-variant. However, due to the low latency requirements and limited computing resources, neural networks may not have enough time and computing resources to execute online training to fine-tune the parameters. This motivates us to design offline-trained neural networks that can perform robustly over wireless channels, but without any actual channel information being known at design time. In this paper, we propose design criteria to generate synthetic training datasets for neural networks, which guarantee that after training the resulting networks achieve a certain mean squared error (MSE) on new and previously unseen channels. Therefore, neural network solutions require no prior channel information or parameters update for real-world implementations. Based on the proposed design criteria, we further propose a benchmark design which ensures intelligent operation for different channel profiles. To demonstrate general applicability, we use neural networks with different levels of complexity to show that the generalization achieved appears to be independent of neural network architecture. From simulations, neural networks achieve robust generalization to wireless channels with both fixed channel profiles and variable delay spreads.