Publication
An SDR-Based Evaluation of Maximum Likelihood Estimator and MUSIC Algorithm for TDOA in 5G Campus Networks
Till Ruppert; Lukas Brechtel; Hans Dieter Schotten
In: Proceedings of the 2025 International Conference on Smart Applications, Communications and Networking (SmartNets). International Conference on Smart Applications, Communications and Networking (SmartNets-2025), 7th, July 22-24, Istanbul, Turkey, IEEE, 2025.
Abstract
In today’s research on Fifth and Sixth Generation
Mobile Networks (5G and 6G), a strong focus is placed on envi-
ronmental sensing, including the localization of User Equipment
(UEs). This study explores Time Difference Of Arrival (TDOA)
estimation based on the Maximum Likelihood (ML) estimator
and the Multiple Signal Classification (MUSIC) algorithm. A
test bed based on an Software Defined Radio (SDR) platform
was developed for evaluation purposes, and Uplink (UL) TDOA
measurements using 5G Sounding Reference Signals (SRSs) were
conducted in an indoor environment within the frequency range
of 5G campus networks. Various factors, including the number of
antennas, transmitter-receiver distance and Signal to Noise Ratio
(SNR), were analyzed. In some measurements, the ML estimator
exhibits higher error than the MUSIC algorithm, primarily due to
multipath components and the ML estimator’s inability to resolve
them temporally. Comparing the pseudospectra, the MUSIC algo-
rithm demonstrates superior temporal resolution compared to the
ML estimator. However, this advantage is not observed across all
measurement series. In low SNR conditions and with an increasing
number of antenna elements, the ML estimator outperforms the
MUSIC algorithm.