Precision ranging technology has become indispensable for ensuring efficient, reliable, and low-latency fifth-generation (5G) networks. In this paper, we propose a novel ranging method which is multipath component (MP...
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Precision ranging technology has become indispensable for ensuring efficient, reliable, and low-latency fifth-generation (5G) networks. In this paper, we propose a novel ranging method which is multipath component (MPC) power delay profile (PDP) based ranging. Whereas the Received Signal Strength (RSS) only summarizes the PDP into a single characteristic, we aim to furthermore exploit the range dependent curvature of the PDP envelope over its delay spread. However, the multipath propagation only allows to sample the PDP envelope at the path delays and suffers from (slow) fading. Hence our approach involves constructing a statistical fading model of the PDP and establishing a relationship between the distribution parameters and the propagation distance. To theoretically validate the feasibility of our proposed method, we adopt the widely accepted Nakagami-m fading model, which renders traditional estimation methods difficult to apply. Therefore we introduce the Expectation Maximization (EM)-revisitedvectorapproximatemessagepassing (ReVAMP) algorithm. This algorithm is specifically designed to handle difficulties in parameter estimation for Gaussian linear models (GLMs) with hidden random variables and intractable posterior distributions. Extensive numerical simulation results have been conducted which exhibit preliminary evidence of the effectiveness of our MPCPDP-based ranging method compared to the received signal strength (RSS)-based method. Moreover, the versatility of the EM-ReVAMP algorithm allows for its extension to other statistical fading models beyond the Nakagami-m model with minor modifications, which opens the door to potential improvements based on more accurate statistical fading models. Nevertheless, the applicability of our MPCPDP-based ranging method should be validated in real-world scenarios in future studies.
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