轻量级扩散模型用于资源受限的语义通信
Lightweight Diffusion Models for Resource-Constrained Semantic Communication
AI总结：
本文提出Q-GESCO框架，通过量化扩散模型减少计算和内存负载，实现语义通信的高效再生，节省75%内存和79%浮点运算。
摘要：
近年来，生成性语义通信模型迅速发展，革新了语义通信框架，提升了性能并开辟了新应用。尽管其能从压缩的语义信息中再生内容，但生成模型在通信系统中面临高内存占用和计算负载的挑战。本文提出Q-GESCO框架，其核心方法是量化语义扩散模型，能够从接收到的语义地图中再生传输图像，同时通过提出的后训练量化技术减少计算负载和内存占用。Q-GESCO对不同信道噪声具有鲁棒性，在不同场景中性能与全精度模型相当，节省高达75%的内存和79%的浮点运算。这使资源受限设备能够利用Q-GESCO的生成能力，扩大生成性语义通信框架的应用范围和系统范围。代码可在https://github.com/ispamm/Q-GESCO获取。
英文摘要：
Recently, generative semantic communication models have proliferated as they are revolutionizing semantic communication frameworks, improving their performance, and opening the way to novel applications. Despite their impressive ability to regenerate content from the compressed semantic information received, generative models pose crucial challenges for communication systems in terms of high memory footprints and heavy computational load. In this paper, we present a novel Quantized GEnerative Semantic COmmunication framework, Q-GESCO. The core method of Q-GESCO is a quantized semantic diffusion model capable of regenerating transmitted images from the received semantic maps while simultaneously reducing computational load and memory footprint thanks to the proposed post-training quantization technique. Q-GESCO is robust to different channel noises and obtains comparable performance to the full precision counterpart in different scenarios saving up to 75% memory and 79% floating point operations. This allows resource-constrained devices to exploit the generative capabilities of Q-GESCO, widening the range of applications and systems for generative semantic communication frameworks. The code is available at https://github.com/ispamm/Q-GESCO.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
