6G Semantic Intelligence for UAV Anti Jamming: A Transformer Augmented Predictive Deep Reinforcement Learning Framework

Abstract

This paper proposes a semantic-aware predictive hybrid Proximal Policy Optimization (PPO) framework for UAV anti-jamming communications under partial observability and nonstationary interference. The framework integrates Transformer-based channel-wise interference prediction, adaptive semantic-rate control, energy-aware horizontal UAV mobility, and hierarchical
discrete–continuous control within a unified POMDP. The semantic payload is explicitly coupled
with achievable rate, transmission duration, delivery success, and propulsion–communication energy,
while predictor learning is separated from the on-policy PPO update. Evaluation over 50 independent
trials shows that, at a jammer power of 30 dBm and CSI error variance of 10-3, the proposed method
achieves a semantic accuracy of 0.751 ± 0.005, a task-success rate of 0.715 ± 0.007, and an SINR
of 6.48 ± 0.28 dB, while consuming 23.58 ± 0.20 J/slot. The closest LSTM-Hybrid PPO baseline
obtains 0.694±0.006, 0.642±0.006, 5.37±0.26 dB, and 25.46±0.17 J/slot, respectively. The Transformer predictor achieves an RMSE of approximately 0.039 under perfect CSI and 0.091 at a CSI
error variance of 10-2. Under an unseen jammer-policy shift, online predictor adaptation recovers
semantic accuracy to approximately 0.859, compared with 0.746 for a frozen predictor. Ablation results indicate complementary contributions from prediction, semantic-rate adaptation, mobility, and
hierarchical hybrid control.
Keywords: Deep Reinforcement Learning, Predictive Anti-Jamming, Semantic Communication,
Transformer-Based Prediction, UAV Networks.

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