OneAI - Novel Multipurpose Deep Learning Algorithms for UWB Wireless Networks

Abstract

In this paper, novel multipurpose deep learning algorithms are proposed for ultra-wideband (UWB) wireless networks that are capable of identifying the channel environment, estimating the SNR level, and performing ToA estimation, simultaneously. UWB technology is among the rapid-growing solutions for the next generation of deep learning-based wireless communication and localization systems. Existing deep learning algorithms for UWB wireless networks have addressed the various signal processing tasks individually in separate deep learning modules. This, however, increases the computational complexity, power consumption, and overall latency of the models. In this paper, unlike the existing methods, the desired signal processing tasks are performed in one single deep learning module. The proposed model consists of a main deep learning module as the core of the model that extracts low-level information from the signal and several shallow learning networks to extract high-level information. We demonstrate that the low-level information that is extracted in the core deep learning module can be reused in all separate tasks. The performance of the proposed models is investigated against the standard IEEE 802.15.4a channel model by evaluating various metrics such as accuracy, area under the curve (AUC), precision, mean absolute error (MAE), and mean square error (MSE).