A neural network-based differentiable CKM construction method enables joint power-bandwidth-trajectory optimization for multi-UAV systems, achieving higher minimum throughput than statistical channel models.
RadioGAT: A joint model-based and data-driven framework for multi-band radiomap reconstruction via graph attention networks
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A physics-aware query-conditioned hierarchical graph attention network estimates point-wise transmitter-resolved radio maps from sparse measurements and outperforms baselines on DeepMIMO simulations in direct, residual, and gated regimes.
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Towards Intelligent Low-Altitude Wireless Network Deployment: Differentiable Channel Knowledge Map Construction and Trajectory Design
A neural network-based differentiable CKM construction method enables joint power-bandwidth-trajectory optimization for multi-UAV systems, achieving higher minimum throughput than statistical channel models.
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Physics-Aware Query-Conditioned Graph Attention Networks for Radio Map Estimation
A physics-aware query-conditioned hierarchical graph attention network estimates point-wise transmitter-resolved radio maps from sparse measurements and outperforms baselines on DeepMIMO simulations in direct, residual, and gated regimes.