Current generation mobile wireless communication networks are not suitable for real-time positioning applications because timing information is not readily available. Fifth generation (5G) cellular networks provide device to device real-time communications which can be used for real-time positioning. Millimeter-wave (mmWave) transmission is regarded as a key technology in 5G networks. In this paper, several 73-GHz mmWave waveforms are investigated. A new threshold selection algorithm for energy detector-based ranging is proposed which employs a dynamic threshold based on an artificial neural network. The positioning performance using this algorithm with mmWave waveforms is investigated.
Because of the good penetration into many common materials and inherent fine resolution, Ultra-Wideband (UWB) signals are widely used in remote sensing applications. Typically, accurate Time of Arrival (TOA) estimation of the UWB signals is very important. In order to improve the precision of the TOA estimation, a new threshold selection algorithm using Artificial Neural Networks (ANN) is proposed which is based on a joint metric of the skewness and maximum slope after Energy Detection (ED). The best threshold based on the signal-to-noise ratio (SNR) is investigated and the effects of the integration period and channel model are examined. Simulation results are presented which show that for the IEEE802.15.4a channel models CM1 and CM2, the proposed ANN algorithm provides better precision and robustness in both high and low SNR environments than other ED-based algorithms.
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