Research On Ranging Algorithm Based On Opportunistic Signals From 5G Macro Base Stations

As urban modernization continues to progress,high precision positioning technology is becoming increasingly important in the urban environment.At present,pedestrian positioning relies on the Global Navigation Satellite System(GNSS)for implementation.However,in urban environments such as high-rise buildings,overpasses,tunnels,and underground parking lots,GNSS signals can be obstructed or even fail to be received,resulting in the inability to guarantee real-time and continuous location services in urban settings.Consequently,there is a need for a high-precision,low-cost,and easily implementable positioning technology and solution for urban environments.In recent years,opportunistic signal-based positioning technology has provided a new approach to addressing urban positioning challenges.Opportunistic signals refer to wireless signals present in the environment that can be used for positioning.However,positioning methods based on opportunistic signals from sources such as Wi-Fi,Bluetooth,and Zigbee require the deployment of base stations in the area to be positioned,followed by the collection of fingerprint information in advance for fingerprint matching or geometric distance measurement to obtain location information.This process is characterized by a high degree of difficulty,complex steps,and increased costs.With the maturation and popularization of 5G technology,5G cellular networks have essentially covered urban areas.Abundant 5G base stations and consistently stable periodic broadcast signals,along with wide bandwidth and low latency,endow 5G signals with vast positioning potential,offering a new solution for pedestrian positioning in urban environments.5G opportunistic signal ranging serves as the foundation for positioning.This paper mainly focuses on the research of 5G opportunistic signal ranging algorithm performance and positioning data compression.Firstly,in estimating the Time of Arrival of 5G opportunistic signals,the paper addresses the issues of inaccurate and unstable delay information in the TOA estimation process with time-domain cross-correlation algorithms and Multiple Signal Classification(MUSIC)algorithms.We propose an improved MUSIC+ELDLL(Early-Late Delay Locked Loop)TOA estimation method.This method utilizes the assistance of pilot transmissions from the Physical Broadcast Channel(PBCH)to analyze and accurately assess and track the delay of captured signals.The algorithm combines MUSIC and the Early-Late power-based phase-locked loop algorithm,with improvements made to the phase-locked loop,resulting in the improved MUSIC+ELDLL algorithm.This algorithm simplifies the phase-locked loop structure while providing the phase-locked loop with a priori information of the phase detector through simulation,thereby enhancing ranging accuracy and effectively improving the stability of the calculated results.Secondly,due to the large amount of positioning data from 5G signals that meet the Nyquist sampling rate with wide bandwidth,there is a high demand for storage and transmission capabilities of hardware devices.To address the issue of severe spectral leakage in reconstructed data from convex optimization algorithms and greedy iterative reconstruction algorithms,which leads to low ranging accuracy,we propose a frequency point constraint-based compressive sensing algorithm using convex optimization.This algorithm takes full advantage of the sparse characteristics of the frequency domain subcarriers in 5G downlink broadcast signals,and by adding constraint conditions to limit the solution space,it obtains the optimal solution that meets the constraint conditions.While ensuring positioning accuracy,this algorithm significantly reduces the storage and data transmission burden on the front-end hardware.Finally,we designed a simulation verification platform based on the Universal Software Radio Peripheral(USRP)to conduct practical tests on the performance of the aforementioned algorithms.Tests on 5G macro base station opportunistic signals were conducted in actual urban environments.The test results indicate that the proposed ranging algorithm’s accuracy and stability have both improved,and the proposed compressive sensing algorithm has significantly reduced data storage and transmission capacity while ensuring ranging accuracy.