6G中的媒体与通信：基础、关键技术及应用
Media Meets Communication in 6G: Fundamentals, Key Technologies, and Applications
浏览论文内容
中文总结 AI 辅助
该文针对6G视觉通信的媒体通信技术开展系统性综述，提出含四大维度的统一框架，涵盖AI驱动媒体技术与感知媒体的无线传输等内容，为6G媒体通信研究提供支撑。
中文摘要 AI 辅助
第六代（6G）网络的快速发展正推动媒体智能与通信智能的融合，促使媒体通信从传统的比特级传输向智能、语义感知及生成式范式演进。新兴媒体服务不仅需要高数据速率与低时延，还需具备语义感知、感知质量保障、自适应资源编排、可信内容处理及个性化媒体生成能力。与此同时，媒体技术正从手工信号处理与传统编码向人工智能（AI）驱动的表示学习、内容理解及生成式重构方向发展。受这些趋势驱动，本文针对6G视觉通信中的媒体通信技术开展系统性综述，回顾通信与媒体技术的演进历程，明确媒体内容处理与无线传输的内在关联。文中引入由四个关键维度构成的统一框架：AI驱动的媒体技术、感知媒体的无线传输、大模型支持的媒体通信及智能网络基础设施。具体而言，AI驱动的媒体技术涵盖媒体编码、内容理解、质量评估、安全与合规检测，以及AIGC支持的媒体生成；感知媒体的无线传输从三个互补视角展开研究：联合编码任务相关语义信息的语义联合信源信道优化、利用媒体特性进行信道适配、预测与补偿的感知信源传输优化，以及基于实时信道条件调整媒体编码与重构的感知信道信源优化。
英文摘要
The rapid advancement of sixth-generation (6G) networks is accelerating the convergence of media intelligence and communication intelligence, driving media communication beyond conventional bit-level delivery toward intelligent, semantic-aware, and generative paradigms. Emerging media services require not only high data rates and low latency, but also semantic awareness, perceptual quality assurance, adaptive resource orchestration, trustworthy content processing, and personalized media generation. Meanwhile, media technologies are evolving from handcrafted signal processing and conventional coding toward artificial intelligence (AI)-driven representation learning, content understanding, and generative reconstruction. Motivated by these trends, this paper presents a systematic survey of media communication technologies for 6G vision communication by revisiting the evolution of communication and media technologies and clarifying the intrinsic relationship between media content processing and wireless transmission. We introduce a unified framework consisting of four key dimensions: AI-driven media technologies, media-aware wireless transmission, large model-enabled media communication, and intelligent network infrastructures. Specifically, AI-driven media technologies encompass media coding, content understanding, quality assessment, security and compliance detection, and AIGC-enabled media generation, while media-aware wireless transmission is examined from three complementary perspectives: semantic joint source-channel optimization, which jointly encodes task-relevant semantic information; source-aware transmission optimization, which leverages media characteristics for channel adaptation, prediction, and compensation; and channel-aware source optimization, which adapts media coding and reconstruction based on real-time channel conditions.
