Moving away from plain-text DNS communications, users can now switch to encrypted DNS protocols for name resolutions. DNS-over-QUIC (DoQ) employs QUIC - the latest transport protocol - for encrypted communications between users and their recursive DNS servers. QUIC is also poised to become the foundation of our daily web browsing by becoming the transport for HTTP/3, the latest version of the HTTP protocol. Traditional TCP-based web browsing is vulnerable to website fingerprinting (WFP) attacks that can identify the websites a user visits. The emergence of QUIC-based DNS and HTTP protocols raises an important question: are regular users better protected from WFP attacks when using these new protocols? To investigate this, we first collect and publicly release the first benchmark dataset of network traffic corresponding to real visits to QUIC-enabled websites while using DoQ for domain resolution. This dataset will help advance the research on WFP attacks and defenses. Second, we implement and evaluate the first WFP attack targeting the combined use of DoQ and HTTP/3 protocols by users by developing two transformer models tailored for WFP attacks. Finally, we conduct comprehensive experiments, which reveal that these models are effective in identifying user-visited websites, emphasizing the need for defensive measures.

This article presents the European 6G Flagship project Hexa-X-II's view on 6G AI-driven air interface. It outlines motivations for AI in the physical layer, selected applications of AI for communication and sensing, their achieved performance, and the challenges to be addressed. The article also provides an overview of the relevant standardization activities.

We introduce a prototyping testbed – Gen-SC-6G – developed to generate a comprehensive dataset that supports the integration of generative artificial intelligence (AI), quantum computing, and semantic communication for emerging sixth generation (6G) applications. The GenSC-6G dataset is designed with noise-augmented synthetic data optimized for semantic decoding, classification, and localization tasks, significantly enhancing flexibility for diverse AI-driven communication applications. This adaptable prototype supports seamless modifications across baseline models, communication modules, and goal-oriented decoders. Case studies demonstrate its application in lightweight classification, semantic upsampling, and edge language inference under noise conditions. The GenSC-6G dataset serves as a scalable and robust resource for developing goal-oriented communication systems tailored to the growing demands of 6G networks.

Recently emerged 6G space-air-ground integrated networks (SAGINs), which integrate satellites, aerial networks, and terrestrial communications, offer ubiquitous coverage for various mobile applications. However, the highly dynamic, open, and heterogeneous nature of SAGINs poses severe security issues. Forming a defense line of SAGINs suffers from two preliminary challenges: accurately understanding massive unstructured multi-dimensional threat information to generate defense strategies against various malicious attacks, and rapidly adapting to potential unknown threats to yield more effective security strategies. To tackle the above two challenges, we propose a novel security framework for SAGINs based on large language models (LLMs), which consists of two key ingredients: LLM-6GNG and 6G-INST. Our proposed LLM-6GNG leverages refined chain-of-thought (CoT) reasoning and dynamic multi-agent mechanisms to analyze massive unstructured multi-dimensional threat data and generate comprehensive security strategies, thus addressing the first challenge. Our proposed 6G-INST relies on a novel self-evolving method to automatically update LLM-6GNG, enabling it to accommodate unknown threats under dynamic communication environments, thereby addressing the second challenge. Additionally, we prototype the proposed framework with ns-3, OpenAirInterface (OAI), and software-defined radio (SDR). Experiments on three benchmarks demonstrate the effectiveness of our framework. The results show that our framework produces highly accurate security strategies that remain robust against a variety of unknown attacks. We will release our code to contribute to the community.

Federated learning (FL) provides a privacy-preserving solution for fine-tuning pretrained large language models (LLMs) using distributed private datasets, enabling task-specific adaptation while preserving data privacy. However, fine-tuning the extensive parameters in LLMs is particularly challenging in resource-constrained federated scenarios due to the significant communication and computational costs. To gain a deeper understanding of how these challenges can be addressed, this article conducts a comparative analysis three advanced federated LLM (FedLLM) frameworks that integrate knowledge distillation (KD) and split learning (SL) to mitigate these issues: 1) FedLLMs, where clients upload model parameters or gradients to enable straight-forward and effective fine-tuning; 2) KD-FedLLMs, which leverage KD for efficient knowledge sharing via logits; and 3) Split-FedLLMs, which split the LLMs into two parts, with one part executed on the client and the other one on the server, to balance the computational load. Each framework is evaluated based on key performance metrics, including model accuracy, communication overhead, and client-side computational load, offering insights into their effectiveness for various federated fine-tuning scenarios. Through this analysis, we identify framework-specific optimization opportunities to enhance the efficiency of FedLLMs and discuss broader research directions, highlighting open opportunities to better adapt FedLLMs for real-world applications. A use case is presented to demonstrate the performance comparison of these three frameworks under varying configurations and settings.

The sixth generation (6G) wireless communication system will improve the key technical indicators by one to two orders of magnitude, and come with new features. As a crucial technique to enhance the reliability and efficiency of data transmission, the next-generation channel coding is thus confronted with new challenges in terms of complexity, latency, and performance. This article supplies an overview of the potential channel codes for 6G communications. In addition, we explore the development of next-generation channel codes based on low-density parity-check (LDPC) and polar frameworks, introducing the concept of generalized LDPC with polar-like component (GLDPC- PC) codes. The codes have exhibited promising error correction performance and manageable complexity, which can be further optimized by specific code design. The opportunities and challenges of GLDPC-PC codes are also discussed.

Natural disasters often disrupt communication networks and severely hamper emergency response and disaster management. Existing solutions, such as portable communication units and cloud-based network architectures, have improved disaster resilience but fall short if both the Radio Access Network (RAN) and backhaul infrastructure become inoperable. To address these challenges, we propose a demand-driven communication system supported by High Altitude Platform Stations (HAPS) to restore communication in an affected area and enable effective disaster relief. The proposed emergency response network is a promising solution as it provides a rapidly deployable, resilient communications infrastructure. The proposed HAPS-based communication can play a crucial role not only in ensuring connectivity for mobile users but also in restoring backhaul connections when terrestrial networks fail. As a bridge between the disaster management center and the affected areas, it can facilitate the exchange of information in real time, collect data from the affected regions, and relay crucial updates to emergency responders. Enhancing situational awareness, coordination between relief agencies, and ensuring efficient resource allocation can significantly strengthen disaster response capabilities. In this article, simulations show that HAPS with hybrid optical/THz links boosts backhaul capacity and resilience, even in harsh conditions. HAPS-enabled RAN in S- and Ka-bands ensures reliable communication for first responders and disaster-affected populations. This article also explores the integration of HAPS into emergency communication frameworks and standards, as it has the potential to improve network resilience and support effective disaster management.

Wireless channel modeling is critical to understanding and optimizing signal transmission. Wireless channels are influenced by many factors, including path loss, reflection, fading, and interference, making them complex and difficult to model and predict. Although some traditional wireless channel modeling methods are effective, they have limitations in handling complex multipath effects and nonlinear characteristics. Generative artificial intelligence (GAI) has the ability to generate robust data and therefore can be potentially used to model realistic wireless channel characteristics. However, there exist some challenges in GAI for wireless channel modeling, including physical layer network security issues, limited generalization ability, and the bandwidth consumption of centralized training. This article introduces a GAI-based wireless channel modeling framework, which leverages blockchain and federated learning to address efficiency, network security, and data privacy concerns in GAI channel modeling. Blockchain technology, through distributed ledgers and smart contracts, enables resource sharing and efficient utilization, enhancing computational efficiency and reducing network security risks. Federated learning allocates training tasks to different devices and nodes, thus allowing model training without centralizing data, protecting user privacy, and reducing network bandwidth usage. This article demonstrates the performance and effectiveness of the proposed framework through numerical results.

The advancement of Artificial Intelligence (AI) has rendered robots an appealing paradigm for various applications, such as parcel sorting and handling in warehouse management. In this context, rapidly evolving mobile communication technology facilitates robot cooperation for efficient parcel sorting and handling. However, traditional AI solutions often struggle with unreasonable resource utilization, which makes it challenging to achieve both accurate parcel sorting and low-latency parcel handling. To address this issue, we propose a Foundation Model (FM)-empowered Open Radio Access Network (O-RAN) framework to achieve highly accurate and real-time robot cooperation in a pipeline manner. We first propose an adaptive FM splitting algorithm that decouples the sorting mission into multiple tasks to enable the robots to perform sequential task implementation for high-accuracy parcel sorting. Next, we present a cooperative path planning algorithm that allows a feasible number of robots to implement cooperative parcel handling with low latency and reduced energy consumption. Finally, we demonstrate the effectiveness of our proposed framework. The simulation results indicate that our solution achieves parcel sorting accuracy of up to 90% while reducing parcel handling latency by an average of 13.9% compared to traditional FM solutions.