Deep Learning Noncoherent UWB Receiver Design

Ultrawideband (UWB) is a promising technology for positioning and wireless communications in Internet-of-things (IoT) applications. However, UWB system's performance is limited by multiple interferences for low complexity noncoherent signal detection methods. Further, deep learning (DL)-based solutions have been envisioned for wireless communications in inaccurate system modeling scenarios. In this letter, we propose a deep learning noncoherent (DLN) UWB receiver to overcome the effect of various interferences such as multiuser interference (MUI), narrowband interference (NBI), and intersymbol-interference (ISI). The DLN is trained offline using the UWB channel statistics, and then, it is used online for data symbol detection. The proposed DLN efficiently learns a nonlinear relationship between input and output in an interference scenario and gives highly accurate data symbol detection, even for training data obtained in a very short period. Numerical results clearly show the proposed DLN UWB receiver's superiority, especially MUI, NBI, and ISI scenarios, over the conventional noncoherent detection method.

Automated summary

The letter proposes a deep learning noncoherent UWB receiver to overcome the impact of various interferences, with numerical results showing its superiority over conventional methods in terms of accurate data symbol detection in interference scenarios.