Method reduces data loss when fixing corrupted channel maps
Structuring and Merging Deletion Demands on Crowdsourced Channel Knowledge Maps
Networking and Internet Architecture
Summary
Wireless base stations keep maps of signal channels using data from many devices, but errors can corrupt parts of these maps. The authors introduce a way to selectively erase only the corrupted parts without losing good data by identifying exactly what needs deletion and merging multiple cleanup tasks. Their method, called SWEep, carefully clears affected data and rechecks the correct areas, saving much more good information compared to removing everything from a faulty device. Tests in simulations and with real hardware show it reduces data loss dramatically.
What this means in practice
- •For wireless network operators: Improve maintenance of channel maps by efficiently deleting corrupted data while preserving good measurements, reducing downtime and improving connection quality.
- •For spectrum engineers: Optimize channel data management by merging deletion requests to minimize redundant resounding and preserve measurement accuracy.
Authors
Zihao Ding, Jun Huang
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.