Doppler-Adaptive Radio Waveform Engine for Highly Dynamic FANETs Using Time-fused Transformer Reinforcement Learning

Flying ad hoc networks (FANETs), consisting of unmanned aerial vehicles (UAV), and aircraft have become popular in the 5G era. However, a fast-response adaptive waveform control mechanism tailored for FANET applications has not yet been deeply investigated. In various scenarios, such as slow but crowded drone swarming networks for search and rescue, fast drone-show networks, and aircraft formations, it is a significant challenge to ensure that the RF modulation method can timely cope with the Doppler effect caused by node mobility changes. This paper introduces a Doppler adaptive waveform engine (DAWE) to adjust the node’s modulation method and parameters, according to the highly dynamic FANET conditions. An enhanced orthogonal time frequency space (OTFS) modulation method is implemented in DAWE to mitigate high Doppler shifts and multipath propagation across diverse FANET scenarios. We further propose a time-fused Transformer mechanism-based reinforcement learning (TTRL) algorithm to adjust the modulation parameters. This algorithm can focus on long-range dependencies better than traditional time-series forecast models and does not require the actor-critic dual model structure that is commonly used in deep reinforcement learning. Our simulations show that, across link SNRs of 20 – 40 dB and user speeds ranging from 100 km h¹ (pedestrian UAV loiter) up to 1000 km h¹ (high-speed fixed-wing transit), the DAWE protocol sustains a bit-error rate roughly one order of magnitude ( 10 ×) lower than a conventional orthogonal frequency-division multiplexing(OFDM)-only baseline.