Efficient and Accurate Surrogate-Assisted Electromagnetic Parameter Calibration for 6G Digital Twin Channels

High-fidelity ray-tracing (RT) channel reconstruction is a fundamental step toward building digital twins for the era of 6G wireless communications. However, precise calibration of complex electromagnetic material parameters remains a dual challenge characterized by massive computational overhead and strict accuracy requirements. To overcome this bottleneck, we propose a Surrogate-assisted Grey Wolf Optimizer with Individual Memory (SGWO-IM) algorithm that simultaneously improves computational efficiency and calibration accuracy. In terms of computational efficiency, an online surrogate model is seamlessly embedded into the evaluation workflow for candidate pre-screening, substantially reducing the reliance on highly time-consuming real RT simulations. Regarding calibration accuracy, adaptive convergence and individual memory strategies are incorporated to optimize the global parameter search path, effectively enhancing the consistency between the reconstructed channel and measured data. Validated against measured channel data from a high-density urban scenario, the proposed algorithm requires only 225 real RT simulation calls compared to the 600 calls needed by the standard Grey Wolf Optimizer (GWO), cutting computational overhead by 62.5%. Concurrently, the final Root Mean Square Error (RMSE) is substantially reduced from the 3.65 dB of GWO to 2.97 dB. The results demonstrate that the SGWO-IM algorithm achieves significant advancements in both efficiency and precision, providing a solution that effectively balances efficiency and accuracy for electromagnetic environment reconstruction.

SAGA: A Robust Self-Attention and Goal-Aware Anchor-based Planner for Safe UAV Autonomous Navigation

Agile unmanned aerial vehicle (UAV) navigation in cluttered environments demands a planning architecture that is both computationally efficient and structurally expressive enough to reason over multiple feasible motions. This paper presents SAGA, a robust self-attention and goal-aware anchor-based planner for safe UAV autonomous navigation. SAGA formulates local planning as a one-stage joint regression-and-ranking problem over a fixed lattice of motion anchors. Given a depth image and a body-frame motion state, the planner predicts refined terminal states and planning scores for all anchors in a single forward pass, after which the best candidate is decoded into a dynamically feasible trajectory. The key idea of SAGA is to transform anchor-aligned features into geometry-aware tokens and perform cross-anchor global reasoning with self-attention. To preserve directional structure in the token space, we further introduce a polar positional encoding derived from anchor yaw and pitch. In addition, a goal-aware modulation module injects velocity, acceleration, and target information into the token representation before final score prediction. Experiments in cluttered pillar-map environments under maximum speed settings of 2.0, 3.0, and 4.0~m/s show that SAGA consistently achieves a 100\% success rate, while YOPO drops from 90.91\% to 62.50\%, Ego-planner from 71.43\% to 52.63\%, and Fast-planner from 52.63\% to 38.46\%. Under the 4.0~m/s maximum speed setting, SAGA also improves average safety from 1.9843~m to 2.3888~m and minimum safety from 0.4390~m to 0.7576~m over YOPO, while reducing total flight time from 40.4631~s to 27.4901~s. The comparison with SAGA w/o PPE further shows that explicit polar positional encoding is critical for stable cross-anchor reasoning and safe passage selection in cluttered scenes.