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Beam alignment is essential to compensate for the high path loss in themillimeter-wave (mmWave) Unmanned Aerial Vehicle (UAV) network. The integratedsensing and communication (ISAC) technology has been envisioned as a promisingsolution to enable efficient beam alignment in the dynamic UAV network.However, since the digital identity (D-ID) is not contained in the reflectedechoes, the conventional ISAC solution has to either periodically feed back theD-ID to distinguish beams for multi-UAVs or suffer the beam errors induced bythe separation of D-ID and physical identity (P-ID). This paper presents anovel dual identity association (DIA)-based ISAC approach, the first solutionthat enables specific, fast, and accurate beamforming towards multiple UAVs. Inparticular, the P-IDs extracted from echo signals are distinguished dynamicallyby calculating the feature similarity according to their prevalence, and thusthe DIA is accurately achieved. We also present the extended Kalman filteringscheme to track and predict P-IDs, and the specific beam is thereby effectivelyaligned toward the intended UAVs in dynamic networks. Numerical results showthat the proposed DIA-based ISAC solution significantly outperforms theconventional methods in association accuracy and communication performance.

Specific Beamforming for Multi-UAV Networks: A Dual Identity-based ISAC Approach