LOCAL-AREA URBAN RADIO-PERFORMANCE MAP ESTIMATION IN BLOCKAGE-PRONE ENVIRONMENTS FROM SPARSE MEASUREMENTS
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DOI:
https://doi.org/10.34238/tnu-jst.15602Abstract
Local-area coverage planning around a base station requires radio-performance maps rather than only line-of-sight/non-line-of-sight labels. This study asks how errors in propagation-state estimation affect path-loss, signal-to-noise-ratio, and data-rate maps under a limited measurement budget. A three-layer evaluation pipeline is constructed for blockage-prone urban areas: a geometric layer generates reference propagation states, sparse samples are processed by a k-nearest-neighbor estimator and a k-nearest-neighbor maximum-a-posteriori estimator, and the inferred labels are connected to a standardized channel model to obtain radio-performance maps. In the single test scene, the maximum-a-posteriori estimator increases label accuracy from 88.64% to 95.45%, reduces path-loss mean absolute error from 1.83 dB to 0.63 dB, and reduces rate mean absolute error from 6.08 Mbps to 2.10 Mbps. Across 100 Monte Carlo runs, it improves average accuracy from 85.85% to 89.41% and reduces rate-map error from 8.64 Mbps to 6.02 Mbps. The results show that estimator quality should be validated at the radio-performance layer, because label errors near blockage boundaries directly distort the operational coverage map.Downloads
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