Joint CFO-Channel Estimation under Strong Inter-Cell Interference for Low-Altitude Radio Mapping

Abstract

Extending terrestrial networks into low-altitude airspace is a practical way to support aerial services, and accurate low-altitude radio maps are essential for characterizing terrestrial base station (BS) coverage and guiding system design. This work targets per-cell per-beam radio mapping from 5G new radio (NR) synchronization signal (SS) burst sets. Conventional processing treats interference as noise and focuses on the strongest link, which is insufficient to comprehensive awareness of the radio environment and ineffective in dense multi-cell low-altitude scenarios. We propose a successive waveform reconstruction and cancellation framework that iteratively estimates, reconstructs, and subtracts the SSs of stronger BSs, thereby enabling reliable detection and estimation of ultra-weak signals. To support this, we introduce the notion of a carrier frequency offset (CFO)-coherent block within which a common-CFO/per-synchronization signal block (SSB)-channel model holds and design a joint CFO-channel estimator that coherently aggregates multiple SSBs within each CFO-coherent block. We further derive closed-form scaling laws that relate estimation accuracy to unmanned aerial vehicle (UAV) speed, motion geometry, burst periodicity, and the length of the CFO-coherent block. Simulations show that the proposed framework can detect and estimate SSs at signal-to-interference-and-noise ratio (SINR) levels down to -30 dB. Field tests at 150 m altitude demonstrate per-beam coverage maps for more than ten overlapping BSs and reveal that, despite strong received power, the measured SINR rarely exceeds 10 dB, underscoring the need for careful interference management in low-altitude airspace.