Structuring and Merging Deletion Demands on Crowdsourced Channel Knowledge Maps

Zihao Ding; Jun Huang

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arXiv · 2610.22589

Structuring and Merging Deletion Demands on Crowdsourced Channel Knowledge Maps

Zihao Ding·Jun Huang

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Abstract

A base station can build a channel knowledge map (CKM) from the uplink pilots of its terminals and sweep only the beams that the map ranks highest. A fault at a terminal or at the base station corrupts the measurements taken while it lasts, and the map must then forget them. Removing the whole terminal discards its good measurements, and the raw reports needed to remove samples are gone. We define the structured deletion unit (SDU), which names the terminals, rounds, grids, and bands to forget. We propose \underline{S}tructured \underline{W}iping with \underline{E}nvelope-bounded \underline{E}xecution \underline{P}artitioning (\method{}), which executes an SDU by clearing blocks of an aggregated ledger and re-sounding the affected grids. \method{} merges pending demands so that one campaign serves several, and it bounds what the smallest SDU containing a group clears beyond the demands. We prove that a merged execution leaves no named measurement in the map, that demands differing only in their intervals are best merged in contiguous runs, and that the greedy merge is within a factor $1+\ln Q$ of the best grouping over its candidates. In simulation and on a USRP testbed, \method{} clears at most 5.1\% of the good measurements, against up to 86.8\% when whole terminals are removed or the map is rolled back to an earlier block.

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BibTeX RIS

Zihao Ding, Jun Huang. 2026-10-15. Structuring and Merging Deletion Demands on Crowdsourced Channel Knowledge Maps. https://arxiv.org/abs/2610.22589

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