[简介] 本文探讨了人工智能（AI）在网络技术中的广泛应用及其对网络重构的贡献。AI技术的引入开启了AI终端时代，支持离线运行和个性化服务，显著提升了用户体验。在网络基础设施方面，以5G/6G、IPv6和算力网为例，AI通过动态频谱分配、智能调度算法等技术，实现提质增效。AI在车联网、卫星通信、工业互联网等领域的应用也取得了显著成效，优化了交通管理、卫星资源利用和工业自动化流程。此外，AI在网络安全领域的应用提升了网络攻击防御能力和数据安全管理效率。尽管面临挑战，AI在网络重构中的重要作用不可忽视，未来AI原生网络的发展将成为重要方向。

[简介]低空经济的迅猛发展对面向低空的无线覆盖扩展提出了挑战。利用现有地面和空天通信系统为低空空域提供通信覆盖，联合智能超表面等技术辅助低空通信将是未来的研究热点。梳理了低空立体无线覆盖技术，指出了低空经济未来的研究方向。认为低空通信将耦合感知、定位功能，提升面向低空空域的管控和服务能力，实现低空智能互联与智能协同，促进低空经济的进一步发展。

[Introduction] Due to the broadcast nature of wireless channels and the development of quantum computers, the confidentiality of wireless communication is seriously threatened. In this paper, we propose an integrated communications and security (ICAS) design to enhance communication security by using reconfigurable intelligent surfaces (RIS), in which the physical layer key generation (PLKG) rate and the data transmission rate are jointly considered. Specifically, to deal with the threat of eavesdropping attackers, we focus on studying the simultaneous transmission and key generation (STAG) by configuring the RIS phase shift. Firstly, we derive the key generation rate of the RIS-assisted PLKG and formulate the optimization problem. Then, considering the dynamic wireless environments, the optimization problem is modeled as a finite Markov decision process. We put forward a policy gradient-based proximal policy optimization (PPO) algorithm to optimize the continuous phase shift of the RIS, which improves the convergence stability and explores the security boundary of the RIS phase shift for STAG. The simulation results demonstrate that the proposed algorithm outperforms the benchmark method in convergence stability and system performance. By reasonably allocating the weight factors for the data transmission rate and the key generation rate, “one-time pad” communication can be achieved. The proposed method has about 90% performance improvement for “one-time pad” communication compared with the benchmark methods.

[Introduction] Along with the proliferating research interest in semantic communication (SemCom), joint source channel coding (JSCC) has dominated the attention due to the widely assumed existence in efficiently delivering information semantics. Nevertheless, this paper challenges the conventional JSCC paradigm and advocates for adopting separate source channel coding (SSCC) to enjoy a more underlying degree of freedom for optimization. We demonstrate that SSCC, after leveraging the strengths of the Large Language Model (LLM) for source coding and Error Correction Code Transformer (ECCT) complemented for channel coding, offers superior performance over JSCC. Our proposed framework also effectively highlights the compatibility challenges between SemCom approaches and digital communication systems, particularly concerning the resource costs associated with the transmission of high-precision floating point numbers. Through comprehensive evaluations, we establish that assisted by LLM-based compression and ECCT-enhanced error correction, SSCC remains a viable and effective solution for modern communication systems. In other words, separate source channel coding is still what we need.