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High Accurate Time-of-Arrival Estimation with Fine-Grained Feature Generation for Internet-of-Things Applications

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Document pages: 6 pages

Abstract: Conventional schemes often require extra reference signals or morecomplicated algorithms to improve the time-of-arrival (TOA) estimationaccuracy. However, in this letter, we propose to generate fine-grained featuresfrom the full band and resource block (RB) based reference signals, andcalculate the cross-correlations accordingly to improve the observationresolution as well as the TOA estimation results. Using the spectrogram-likecross-correlation feature map, we apply the machine learning technology withdecoupled feature extraction and fitting to understand the variations in thetime and frequency domains and project the features directly into TOA results.Through numerical examples, we show that the proposed high accurate TOAestimation with fine-grained feature generation can achieve at least 51 rootmean square error (RMSE) improvement in the static propagation environments and38 ns median TOA estimation errors for multipath fading environments, which isequivalently 36 and 25 improvement if compared with the existing MUSIC andESPRIT algorithms, respectively.

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