LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications
2025/07/23 by Chunmei Xu, Siqi Zhang, Xu, Chunmei +5 · 1 citation
Social Sciences · #Coding (social sciences) #Decoding methods #Encoding (memory) #FOS: Electrical engineering #Generative grammar #Key (lock) #Multimedia Communication and Technology #Robustness (evolution) #Signal Processing (eess.SP) #Transmitter #Wireless #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2507.17352
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2025/07/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
Data-intensive and immersive applications, such as virtual reality, impose stringent quality of experience (QoE) requirements that challenge traditional quality of service (QoS)-driven communication systems. This paper presents LightCom, a lightweight encoding and generative AI (GenAI)-augmented decoding framework, designed for QoE-oriented communications under low signal-to-noise ratio (SNR) conditions. LightCom simplifies transmitter design by applying basic low-pass filtering for source coding and minimal channel coding, significantly reducing processing complexity and energy consumption. At the receiver, GenAI models reconstruct high-fidelity content from highly compressed and degraded signals by leveraging generative priors to infer semantic and structural information beyond traditional decoding capabilities. The key design principles are analyzed, along with the sufficiency and error-resilience of the source representation. We also develop importance-aware power allocation strategies to enhance QoE and extend perceived coverage. Simulation results demonstrate that LightCom achieves up to a 14 dB improvement in robustness and a 9 dB gain in perceived coverage, outperforming traditional QoS-driven systems relying on sophisticated source and channel coding. This paradigm shift moves communication systems towards human-centric QoE metrics rather than bit-level fidelity, paving the way for more efficient and resilient wireless networks.
Citations
- ResiTok: A Resilient Tokenization-Enabled Framework for Ultra-Low-Rate and Robust Image Transmission
- Data-Importance-Aware Power Allocation for Adaptive Semantic Communication in Computer Vision Applications
- Data-Importance-Aware Waterfilling for Adaptive Real-Time Communication in Computer Vision Applications
- Separate Source Channel Coding Is Still What You Need: An LLM-based Rethinking
- Generative Semantic Communications with Foundation Models: Perception-Error Analysis and Semantic-Aware Power Allocation
- Multimodal Semantic Communication for Generative Audio-Driven Video Conferencing
- Semantic-Aware Power Allocation for Generative Semantic Communications with Foundation Models
- Latency-Aware Generative Semantic Communications with Pre-Trained Diffusion Models
- Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
- Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions
- Large Generative AI Models for Telecom: The Next Big Thing?
- On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds
- On the Road to 6G: Visions, Requirements, Key Technologies, and Testbeds
- Adding Conditional Control to Text-to-Image Diffusion Models
- Adding Conditional Control to Text-to-Image Diffusion Models
- Generative Joint Source-Channel Coding for Semantic Image Transmission
- Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications
- Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges
- High-Resolution Image Synthesis with Latent Diffusion Models
- High-Resolution Image Synthesis with Latent Diffusion Models
- Learning Transferable Visual Models From Natural Language Supervision
- Semantic Communication Systems for Speech Transmission
- Semantic Communication Systems for Speech Transmission
- Language Models are Few-Shot Learners
- Toward 6G Networks: Use Cases and Technologies
- Making a “Completely Blind” Image Quality Analyzer
- Выпуклая оптимизация
- Overview of the H.264/AVC video coding standard
- The JPEG still picture compression standard
Cited by
Related