Enhancing Neural Receiver Performance through Novel Training Strategies

Modern communication systems, particularly those utilizing Orthogonal Frequency Division Multiplexing (OFDM), rely on efficient data transmission. However, traditional receiver techniques, which rely on orthogonal pilot signals, often suffer from overhead and inefficiencies in dynamic environments [1].This research investigates the integration of deep learning, specifically neural networks, into receiver chains to enhance adaptability and robustness in dynamic environments. A critical challenge in this area is dataset generalization, as machine learning models often lack sufficient real-world wireless data for training. Building upon existing literature [2], we explore whether ray tracing can be used to generalize neural receivers, enabling them to perform effectively in dynamic conditions.Drawing from established works, including Nvidia’s Neural Multi-User MIMO OFDM Receiver [3] and DeepRx [4], this study focuses on training neural receivers to adapt dynamically to diverse channel conditions. The goal is to investigate novel training approaches that consistently achieve lower bit error rates (BER). By leveraging hybrid training strategies and ray tracing based training, this research advances neural receiver technology for real-time applications in 5G and beyond.