ChannelLM-driven digital twin architecture reduces channel prediction error by 4.23 dB in unseen environments versus small AI models while achieving 70 ms end-to-end latency.
Channel measurement, modeling, and simulation for 6G: A survey and tutorial
5 Pith papers cite this work. Polarity classification is still indexing.
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Under equal physical aperture, 15 GHz FR3 measurements show higher spectral efficiency than 8 GHz due to more antenna elements overcoming increased sparsity, despite a 3 dB coverage deficit.
Under a dynamic generalized channel model, OFDM and DFT-s-OFDM can be tuned for superior reliability and stability compared to AFDM and OTFS, which only retain advantages in sparse stationary settings.
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.
citing papers explorer
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Paradigm Shift from Statistical Channel Modeling to Digital Twin Prediction: An Environment-Generalizable ChannelLM for 6G AI-enabled Air Interface
ChannelLM-driven digital twin architecture reduces channel prediction error by 4.23 dB in unseen environments versus small AI models while achieving 70 ms end-to-end latency.
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Measurement-Based Massive MIMO Channel Characterization and Performance Evaluation at FR3 (8 and 15 GHz) Under Equal Physical Aperture
Under equal physical aperture, 15 GHz FR3 measurements show higher spectral efficiency than 8 GHz due to more antenna elements overcoming increased sparsity, despite a 3 dB coverage deficit.
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Channel-Aware Waveform Selection Criteria Across Different Waveform Domains
Under a dynamic generalized channel model, OFDM and DFT-s-OFDM can be tuned for superior reliability and stability compared to AFDM and OTFS, which only retain advantages in sparse stationary settings.
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Generative Channel Knowledge Base With Environmental Information for Joint Source-Channel Coding in Semantic Communications
A Transformer-based generative model builds an environment-aware channel knowledge base that is injected into JSCC encoders and decoders, achieving 10^{-3} level channel estimation error and outperforming benchmarks in semantic communication performance.
- Urban Macro/Microcellular Channel Characterization at 4.85 GHz With Literature-Referenced Upper-FR1-to-FR3 Cross-Band Analysis