The next generation (6G) wireless systems aim to cater to the Internet of Everything (IoE) and revolutionize customer services and applications to a fully intelligent and autonomous system. To achieve this, the digital twin edge network (DITEN) is proposed to combine mobile/multi-access edge computing (MEC) and digital twin (DT), thereby improving the network performance such as throughput and security, and reducing the cost of communication, computation, and caching. In DITENs, the network status can be continuously monitored, and based on the obtained network states, the networking schemes, such as routing and resource management, can be studied in the established DITENs from a centralized perspective. In this survey, we present a comprehensive overview of DITEN for 6G. First, we present the fundamental aspects of DITEN, including concept, framework, and potential. Second, a comprehensive design of DITEN is devised, including the DT modeling/updating, DT deployment, key issues, and enabling technologies. Then, the typical applications of DITEN towards 6G are provided, including the Internet of Things (IoT), vehicular network, space-air-ground integrated network (SAGIN), healthcare, wireless systems, and other applications, along with the design of DITEN in each application, such as DT modeling, DT association, incentive mechanisms, and so on. Finally, challenges and open issues are discussed.

As Large Language Models (LLMs) have achieved significant success in handling multi-modal tasks such as text, images, videos, and sounds, particularly showcasing emergent capabilities in natural language tasks, they hold great potential for network operations that similarly involve vast amounts of text data, fault data, and log files. This paper focuses on the development of LLMs, detailing their fundamental principles and application scenarios across different domains. It highlights the remarkable capabilities of LLMs in tasks such as fault diagnosis, causal inference, and intelligent question answering, and applies these abilities to the field of network operations. Moreover, the paper reviews some of the key issues and technical barriers faced by intelligent networks, such as efficiently monitoring networks in real-time and providing timely alerts when necessary. In addition to examining the utilization of LLM in network operations, this paper introduces a framework for intelligent network operations and performance optimization, leveraging LLM. The objective of this framework is to bolster network robustness and furnish users with exceptional, personalized network services. Ultimately, we conclude by delineating the challenges encountered in LLM-based intelligent network operations and performance optimization, while presenting potential solutions to overcome these hurdles and propel the comprehensive deployment of LLM-driven network intelligence.

In the future era of intelligent networks, communication technology and network architecture need to be further developed to provide users with high-quality services. The Space-Air-Ground Integrated Networks (SAGIN) is seen as a potential architecture to provide ubiquitous communication and drive the era of the intelligent global network. The space and air segments in SAGIN can assist in offloading traffic from the ground segment. However, in a highly dynamic and heterogeneous network like SAGIN, offloading decisions are easily affected by the incorporated/malicious nodes. How to ensure security and improve network performance becomes a critical problem. In this paper, we address the above problem by jointly using blockchain and federated reinforcement learning (FRL). Firstly, we propose a blockchain-based secure federated learning framework that combines topology information chain and model chain to assist traffic offloading. Then, we propose a node security evaluation and an enhanced practical byzantine fault tolerance (EPBFT) algorithm to secure the traffic offloading process. Furthermore, we describe the traffic offloading problem as a Markov decision problem (MDP) and employ the Blockchain-based Federated Asynchronous Advantage Actor-Critic (BFA3C) algorithm to solve this problem. Finally, the simulation results show that the BFA3C-based algorithm used in SAGIN with/without malicious nodes achieves superior performance in terms of latency and security.

Frequency selective surfaces (FSSs) have attracted extensive attention for suppressing interference and improving channel quality and coverage by selectively transmitting or directionally reflecting electromagnetic waves in a specific frequency range in wireless communication networks. Therefore, FSS technology is considered to be another candidate after intelligent reflective surfaces (IRSs) in the sixth generation (6G) communication systems. In this paper, we provide a comprehensive investigation of the theory, design, and classification of FSS, and a contemporary overview of its vision and current applications in future 6G networks, as well as some emerging use cases of FSSs in 6G networks. Then, we describe the fundamentals and design methods of FSS from architecture, performance metrics, and experimental analysis methods and classify the relevant papers according to the FSS applications. Moreover, we review systematically the frequency selection characteristics, reflection, transmission, and absorption properties of FSS, and discuss the corresponding communication models. Additionally, we provide an overview of the vision and requirements for FSS-assisted 6G networks and summarize the FSS communication architecture and performance analysis towards 6G networks. Since the IRS technology is another metasurface candidate for 6G networks, we compare and review them with those for IRS-assisted 6G networks. Furthermore, we review some emerging use cases in 6G networks and present a new use case of adaptive spectrum allocation based on FSS. Finally, based on an extensive literature review and common weaknesses of FSS extant literature, we offer several challenges and some potential research directions.

The sixth generation mobile communication standard (6G) can promote the development of Industrial Internet and Internet of Things (IoT). To achieve comprehensive intelligent development of the network and provide customers with higher quality personalized services. This paper proposes a network performance optimization and intelligent operation network architecture based on Large Language Model (LLM), aiming to build a comprehensive intelligent 6G network system. The Large Language Model, with more parameters and stronger learning ability, can more accurately capture patterns and features in data, which can achieve more accurate content output and high intelligence and provide strong support for related research such as network data security, privacy protection, and health assessment. This paper also presents the design framework of a network health assessment system based on LLM and focuses on its potential application scenarios, through the case of network health management system, it is fully demonstrated that the 6G intelligent network system based on LLM has important practical significance for the comprehensive realization of intelligence.