In this paper, we design a new class of high-efficiency deep joint source-channel coding methods to achieve end-to-end video transmission over wireless channels. The proposed methods exploit nonlinear transform and conditional coding architecture to adaptively extract semantic features across video frames, and transmit semantic feature domain representations over wireless channels via deep joint source-channel coding. Our framework is collected under the name deep video semantic transmission (DVST). In particular, benefiting from the strong temporal prior provided by the feature domain context, the learned nonlinear transform function becomes temporally adaptive, resulting in a richer and more accurate entropy model guiding the transmission of current frame. Accordingly, a novel rate adaptive transmission mechanism is developed to customize deep joint source-channel coding for video sources. It learns to allocate the limited channel bandwidth within and among video frames to maximize the overall transmission performance. The whole DVST design is formulated as an optimization problem whose goal is to minimize the end-to-end transmission rate-distortion performance under perceptual quality metrics or machine vision task performance metrics. Across standard video source test sequences and various communication scenarios, experiments show that our DVST can generally surpass traditional wireless video coded transmission schemes. The proposed DVST framework can well support future semantic communications due to its video content-aware and machine vision task integration abilities.

In a multi-user system, system resources should be allocated to different users. In traditional communication systems, system resources generally include t

The convergence of communication and artificial intelligence (AI) is transforming next-generation wireless networks. Inspired by natural intelligence, particularly in how data, information, and knowledge are processed for cognition, reasoning, learning, decision-making, and environmental interaction, we introduce a new paradigm called ComAI. This paradigm is expected to play a key role in future wireless networks. In this paper, we highlight the motivations behind the convergence of communications and AI and present a roadmap that outlines the evolution of wireless communications alongside advancements in AI. Then, we introduce the key idea of ComAI and provide a general framework of ComAI, which includes information sensing and data collection, information cognition through intellicise semantic communication networks, and information decision with large-scale models. In particular, intellicise semantic communication networks refer to intelligent and concise communication systems that leverage semantic-level information to simplify and restructure signal processing, information cognition, and network organization. We further discuss the foundational theories and key techniques for the convergence of communication and AI. Additionally, we provide experimental validations, including insights from the 6G test network, and highlight potential application scenarios such as embodied AI robots and multi-agent collaborative communications. Finally, we outline the future directions and open issues in the development of ComAI, sharing our thoughts and insights on potential solutions.

As a novel paradigm, semantic communication provides an effective solution for breaking through the future development dilemma of classical communication systems. However, it remains an unsolved problem of how to measure the information transmission capability for a given semantic communication method and subsequently compare it with the classical communication method. In this paper, we first present a review of the semantic communication system, including its system model and the two typical coding and transmission methods for its implementations. To address the unsolved issue of the information transmission capability measure for semantic communication methods, we propose a new universal performance measure called Information Conductivity. We provide the definition and the physical significance to state its effectiveness in representing the information transmission capabilities of the semantic communication systems and present elaborations including its measure methods, degrees of freedom, and progressive analysis. Experimental results in image transmission scenarios validate its practical applicability.

In-band full-duplex (IBFD) is a theoretically effective solution to increase the overall throughput for the future wireless communications system by enabling transmission and reception over the same time-frequency resources. However, reliable source reconstruction remains a great challenge in the practical IBFD systems due to the non-ideal elimination of the self-interference and the inherent limitations of the separate source and channel coding methods. On the other hand, artificial intelligence-enabled semantic communication can provide a viable direction for the optimization of the IBFD system. This article introduces a novel IBFD paradigm with the guidance of semantic communication called semantics-division duplexing (SDD). It utilizes semantic domain processing to further suppress self-interference, distinguish the expected semantic information, and recover the desired sources. Further integration of the digital and semantic domain processing can be implemented so as to achieve intelligent and concise communications. We present the advantages of the SDD paradigm with theoretical explanations and provide visualized results to verify its effectiveness.

Artificial intelligence (AI) is expected to serve as a foundational capability across the entire lifecycle of 6G networks, spanning design, deployment, and operation. This article proposes a native AI-driven air interface architecture built around two core characteristics: compression and adaptation. On one hand, compression enables the system to understand and extract essential semantic information from the source data, focusing on task relevance rather than symbol-level accuracy. On the other hand, adaptation allows the air interface to dynamically transmit semantic information across diverse tasks, data types, and channel conditions, ensuring scalability and robustness. This article first introduces the native AI-driven air interface architecture, then discusses representative enabling methodologies, followed by a case study on semantic communication in 6G non-terrestrial networks. Finally, it presents a forward-looking discussion on the future of native AI in 6G, outlining key challenges and research opportunities.