With the unprecedented surge in demand for flexible deployment and ultra-high
throughput in next-generation wireless networks, airborne base stations (ABS),
particularly unmanned aerial vehicles (UAVs), have become emerged as
indispensable mobile communication infrastructure. To facilitate high-gain
directional transmission within the millimeter-wave band, ABS frequently employ
massive MIMO antenna arrays. However, under random disturbances caused by
non-stationary turbulence, the system faces a severe physical bottleneck: weak
anti-interference capability due to narrow beamwidth, which can easily lead to a
sharp drop in link capacity. Addressing the two major shortcomings of existing
deep learning prediction schemes—susceptibility to sensor noise interference and
negative capacity performance shifts during stable periods, and response lag due
to memory inertia during sudden turbulence periods—this paper proposes an
ARET-ALSTM active compensation network based on SNR-aware multimodal attention,
agile reset and adaptive event-triggered mechanisms. This network incorporates
an event-triggered mechanism governed by a dynamic physical variance threshold,
adaptively executing compensation during stable intervals to prevent excessive
overshoot and redundant computational overhead. Simultaneously, it employs an
agile feature reset mechanism based on an adaptive sliding window for concept
drift detection, forcibly discarding old memories and reconstructing attention
weights during sudden wind shear, achieving millisecond-level ultra-fast
adaptation and directly driving network optimization with the goal of maximizing
communication capacity. Simulation experiments show that, under different array
configurations ranging from 2×2 to 16×16 and complex flow conditions, ARET-ALSTM
successfully overcomes the physical capacity collapse limitation under large
antenna scales, and its tracking agility and robustness are significantly better
than benchmark algorithms such as Transformer, RNN and traditional Kalman
filtering.