Regularised Hyper Parameter Bi Level Optimisation With Continual Learning Based Deep Neural Network for Beamforming in Ultra-Wide Band System

Ultra-Wideband (UWB) is a wireless communication technology that uses Radio Frequency (RF) to transmit and receive signals between devices. Beamforming in UWB is a technique that uses multiple antennas simultaneously to focus on specific directions. In beamforming, Deep Learning (DL) techniques are applied to enhance signal processing and optimise beam pattern generation by utilising neural networks for efficient and accurate spatial filtering. However, existing DL techniques suffer from catastrophic forgetting, in which the testing data forgets previously learnt data due to the lack of knowledge distillation in other layers. Therefore, this research proposes a Regularised Hyperparameter Bilevel Optimisation with Continual Learning-based Deep Neural Network (RHBO-CLDNN) for beamforming in UWB systems. RHBO optimises hyperparameter efficiency at both the upper and lower levels, thereby enabling the DNN to accurately capture UWB channel characteristics, which improves channel estimation and enhances the Signal-to-Noise Ratio (SNR). CL is applied to dynamically adapt to changing environmental conditions without requiring complete retraining, making it suitable for real-time applications. Elastic Weight Consolidation (EWC) regularisation is also applied, which mitigates catastrophic forgetting by preserving weights from learnt tasks and enables the model to adapt to channel conditions without losing previous knowledge. Experiments on the DeepMIMO dataset show that RHBO-CLDNN enhances the sum-rate by up to 18% and achieves an inference time of 0.025 s over Convolutional Neural Network (CNN), thereby demonstrating its suitability for real-time beamforming.