SkillCom decomposes LLM semantic communication into four skills connected by structured semantic-unit interfaces and outperforms monolithic LLM baselines in robustness on multi-hop QA and dialogue state tracking tasks.
Deep learning enabled semantic communication systems
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A Mamba-based semantic video communication system with CSI-guided adaptive encoding and packet loss recovery achieves PSNR > 21 dB at -8 dB SNR and 90% packet loss in AWGN channels.
Proposes MSCT and MSNCT semantic communication frameworks for multi-satellite massive MIMO image transmission, plus a MoCM mixture framework that dynamically switches modes using statistical CSI and shows simulation gains.
XL-MIMO systems with analog combining perform OTA classification via ELM framework achieving over 90% accuracy with few ms latency under rich fading.
The paper derives closed-form minimum achievable rates under semantic distance and complexity constraints for Gaussian and binary sources, demonstrating a fundamental three-way tradeoff validated on image and video data.
citing papers explorer
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SkillCom: Decomposing LLM-based Semantic Communication into Task and Channel Aware Skills
SkillCom decomposes LLM semantic communication into four skills connected by structured semantic-unit interfaces and outperforms monolithic LLM baselines in robustness on multi-hop QA and dialogue state tracking tasks.
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-8 dB SNR + 90% Packet Loss: MamVSC -- CSI-Guided Semantic Mamba for Extreme-Robust Video Semantic Communication
A Mamba-based semantic video communication system with CSI-guided adaptive encoding and packet loss recovery achieves PSNR > 21 dB at -8 dB SNR and 90% packet loss in AWGN channels.
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Semantic Communication for Multi-Satellite Massive MIMO Transmission: A Mixture of Cooperative Modes Framework
Proposes MSCT and MSNCT semantic communication frameworks for multi-satellite massive MIMO image transmission, plus a MoCM mixture framework that dynamically switches modes using statistical CSI and shows simulation gains.
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Universal Approximation with XL MIMO Systems: OTA Classification via Trainable Analog Combining
XL-MIMO systems with analog combining perform OTA classification via ELM framework achieving over 90% accuracy with few ms latency under rich fading.
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On the Rate-Distortion-Complexity Tradeoff for Semantic Communication
The paper derives closed-form minimum achievable rates under semantic distance and complexity constraints for Gaussian and binary sources, demonstrating a fundamental three-way tradeoff validated on image and video data.