LightCom: A Generative AI-Augmented Framework for QoE-Oriented Communications

TL;DR

LightCom introduces a lightweight, AI-augmented communication framework that enhances user experience in data-heavy applications by reconstructing high-quality content from degraded signals, outperforming traditional systems in robustness and coverage.

Contribution

The paper proposes LightCom, a novel QoE-oriented communication framework combining simple encoding with generative AI decoding, reducing complexity and improving robustness under low SNR conditions.

Findings

Achieves up to 14 dB robustness improvement.

Gains 9 dB in perceived coverage.

Outperforms traditional QoS-based systems.

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.