LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.
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SinFormer is a tailored transformer that applies multi-scale self-attention and staged training to improve accuracy and robustness in radio frequency fingerprint identification on real-world data.
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LatencyScope: A System-Level Mathematical Framework for 5G RAN Latency
LatencyScope models 5G RAN latency sources across the protocol stack and provides a configuration analyzer that identifies settings meeting latency-reliability targets, validated on open-source testbeds and commercial network measurements where it outperforms prior models and simulators.
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SinFormer: A Tailored Transformer for Robust Radio Frequency Fingerprint Identification
SinFormer is a tailored transformer that applies multi-scale self-attention and staged training to improve accuracy and robustness in radio frequency fingerprint identification on real-world data.