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Recursive State Estimation for Short-Term V2I Cluster-Delay Prediction

Wanji Hou, Gang Lv, Shuang Liu
Vol. 8, No. 10, pp. 92-99, 2026 DOI: 10.54691/8w78pb71 October 21, 2026
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Abstract.Short-horizon cluster-delay prediction must balance fluctuation suppression against responsiveness while accommodating newly observed clusters. We investigate a scalar local-level Kalman predictor that summarizes past observations recursively instead of requiring a fixed-length input window. Training-only trajectory validation selects its process-to-residual variance ratio; prediction precedes each observation update, and evaluation includes segment startup. Existing NYUSIM trajectories support direction holdouts, speed transfer, and a supplementary configuration-transfer evaluation with frozen parameters. At 25 m/s, the predictor reduces RMSE from 5.625 ns for persistence to 4.354 ns, closely matching a validated moving mean at 4.355 ns. The supplementary errors are 4.577 and 4.580 ns, respectively. Trend and innovation-adaptive extensions provide no consistent benefit. Startup errors and a segment-level counterexample delimit the results. These results characterize the accuracy and startup behavior of a compact recursive predictor within the evaluated stable segments.
Keywords. V2I Channel; Cluster Delay; Recursive Prediction; Kalman Filter; Evaluation.
APA Citation Wanji Hou, Gang Lv, Shuang Liu (2026). Recursive State Estimation for Short-Term V2I Cluster-Delay Prediction. Scientific Journal of Technology, 8(10), 92-99. https://doi.org/10.54691/8w78pb71

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