UWB Signal Detection by Cyclic Features

TL;DR

This paper introduces computationally efficient UWB signal detectors leveraging cyclic features, providing a balance between detection accuracy and low complexity in challenging environments.

Contribution

It reveals closed-form relationships between cyclic features and system parameters, and proposes low-complexity detectors based on cyclic autocorrelation functions.

Findings

Detectors achieve good detection performance in multipath and noisy environments.

Proposed methods have lower computational complexity than existing detectors.

Simulation results confirm effectiveness across various interference scenarios.

Abstract

Ultra-wideband (UWB) impulse radio (IR) systems are well known for low transmission power, low probability of detection, and overlaying with narrowband (NB) systems. These merits in fact make UWB signal detection challenging, since several high-power wireless communication systems coexist with UWB signals. In the literature, cyclic features are exploited for signal detection. However, the high computational complexity of conventional cyclic feature based detectors burdens the receivers. In this paper, we propose computationally efficient detectors using the specific cyclic features of UWB signals. The closed-form relationships between the cyclic features and the system parameters are revealed. Then, some constant false alarm rate detectors are proposed based on the estimated cyclic autocorrelation functions (CAFs). The proposed detectors have low complexities compared to the existing ones. Extensive simulation results indicate that the proposed detectors achieve a good balance between the detection performance and the computational complexity in various scenarios, such as multipath environments, colored noise, and NB interferences.