The emergence of the Metaverse brings new opportunities for enhancing productivity and creativity through real time updates and personalized content. However, it also leads to the generation of massive volumes of dynamic information, placing unprecedented demands on existing communication networks. Current bit-oriented communication systems are not designed to cope with such high levels of semantic complexity and data volume, ultimately limiting the responsiveness and interactivity of Metaverse applications. To address this research gap, we propose a goal-oriented semantic communication framework enabled by generative AI and optimal transport for Metaverse construction (GSC). The proposed GSC framework includes an hourglass network-based (HgNet) encoder to extract semantic information of objects in the Metaverse and a semantic decoder to construct the Metaverse content after wireless transmission,enabling efficient communication and real-time object behaviour updates to the scenery for the Metaverse construction task. To overcome the wireless channel noise at the receiver, we design an optimal transport (OT)-enabled semantic denoiser, which enhances the accuracy of the Metaverse scenery through wireless communication. The results of our computer experiments demonstrate that compared to the conventional Metaverse construction, our proposed GSC framework significantly reduces wireless Metaverse construction latency by 92.6%, while improving the Metaverse object status accuracy and viewing experience by45.6% and 44.7%, respectively.

With the emergence of the metaverse and its role in enabling real-time simulation and analysis of real-world counterparts, an increasing number of personalized metaverse scenarios are being created to influence entertainment experiences and social behaviors. However, compared to traditional image and video entertainment applications, the exact transmission of the vast amount of metaverse-associated information significantly challenges the capacity of existing bit-oriented communication networks. Moreover, the current metaverse also witnesses a growing goal shift for transmitting the meaning behind custom-designed content, such as user-designed buildings and avatars, rather than exact copies of physical objects. To meet this growing goal shift and bandwidth challenge, this paper proposes a goal-oriented semantic communication framework for metaverse application (GSCM) to explore and define semantic information through the goal levels. Specifically, we first analyze the traditional image communication framework in metaverse construction and then detail our proposed semantic information along with the end-to-end wireless communication. We then describe the designed modules of the GSCM framework, including goal-oriented semantic information extraction, base knowledge definition, and neural radiance field (NeRF) based metaverse construction. Finally, numerous experiments have been conducted to demonstrate that, compared to image communication, our proposed GSCM framework decreases transmission latency by up to 92.6% and enhances the virtual object operation accuracy and metaverse construction clearance by up to 45.6% and 44.7%, respectively.

With the emergence of the metaverse and its applications in representing humans and intelligent entities in social and related augmented reality (AR) applications. The current bit-oriented network faces challenges in supporting real-time updates for the vast amount of associated information, which hinders development. Thus, a critical revolution in the sixth generation (6G) networks is envisioned through the joint exploitation of information context and its importance to the goal, leading to a communication paradigm shift towards semantic and effectiveness levels. However, current research has not yet proposed any explicit and systematic communication framework for AR applications that incorporate these two levels. To fill this research gap, this paper presents a goal-oriented semantic communication framework for augmented reality (GSAR) to enhance communication efficiency and effectiveness in 6G. Specifically, we first analyse the traditional wireless AR point cloud communication framework and then summarize our proposed semantic information along with the end-to-end wireless communication. We then detail the design blocks of the GSAR framework, covering both semantic and effectiveness levels. Finally, numerous experiments have been conducted to demonstrate that, compared to the traditional point cloud communication framework, our proposed GSAR significantly reduces wireless AR application transmission latency by 95.6%, while improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.

Multi-vehicle pursuit (MVP) such as autonomous police vehicles pursuing suspects is important but very challenging due to its mission and safety-critical nature. While multi-agent reinforcement learning (MARL) algorithms have been proposed for MVP in structured grid-pattern roads, the existing algorithms use random training samples in centralized learning, which leads to homogeneous agents showing low collaboration performance. For the more challenging problem of pursuing multiple evaders, these algorithms typically select a fixed target evader for pursuers without considering dynamic traffic situation, which significantly reduces pursuing success rate. To address the above problems, this paper proposes a Progression Cognition Reinforcement Learning with Prioritized Experience for MVP (PEPCRL-MVP) in urban multi-intersection dynamic traffic scenes. PEPCRL-MVP uses a prioritization network to assess the transitions in the global experience replay buffer according to each MARL agent’s parameters. With the personalized and prioritized experience set selected via the prioritization network, diversity is introduced to the MARL learning process, which can improve collaboration and task-related performance. Furthermore, PEPCRL-MVP employs an attention module to extract critical features from dynamic urban traffic environments. These features are used to develop a progression cognition method to adaptively group pursuing vehicles. Each group efficiently targets one evading vehicle. Extensive experiments conducted with a simulator over unstructured roads of an urban area show that PEPCRL-MVP is superior to other state-of-the-art methods. Specifically, PEPCRL-MVP improves pursuing efficiency by 3.95$\%$over Twin Delayed Deep Deterministic policy gradient-Decentralized Multi-Agent Pursuit and its success rate is 34.78$\%$higher than that of Multi-Agent Deep Deterministic Policy Gradient. Codes are open-sourced.

Upon the advent of the emerging metaverse and its related applications in Augmented Reality (AR), the current bit-oriented network struggles to support real-time changes for the vast amount of associated information, creating a significant bottleneck in its development. To address the above problem, we present a novel task-oriented and semantics-aware communication framework for augmented reality (TSAR) to enhance communication efficiency and effectiveness significantly. We first present an analysis of traditional wireless AR point cloud communication framework, followed by a detailed summary of our proposed semantic information extraction within the end-to-end communication. Then, we detail the components of the TSAR framework, incorporating semantics extraction with deep learning, task-oriented base knowledge selection, and avatar pose recovery. Through rigorous experimentation, we demonstrate that our proposed TSAR framework considerably outperforms traditional point cloud communication framework, reducing wireless AR application transmission latency by 95.6% and improving communication effectiveness in geometry and color aspects by up to 82.4% and 20.4%, respectively.

The encrypted traffic classification based on federated learning has become one of the key concerns since it can effectively provide expansion and privacy protection for traffic dataset. However, existing classification models suffer from low robustness and slow convergence in the presence of abnormal traffic data on the client side. We note that the clients participating in the training are operated by humans in social networks, and their communication with each other generate social traffic. By introducing the traffic data into the federated learning classification model, the correlation between the respective small model parameters of the clients can be increased, which can be leveraged to quickly detect abnormal clients and improve the model performance. The effectiveness of the scheme is verified on a classical public dataset and the results show that this WS network structure converges the fastest and the degree distribution has an overall linear relationship with the convergence speed. Our scheme is still highly robust with abnormal data and the model convergence speed is significantly better than other methods. Compared with the existing method, the model of social networks based classification of federated encrypted traffic has 2.5 % higher accuracy, 6.1 % higher recall, and more than 39.3% fewer communication rounds, respectively.

With the development of the Internet of Vehicles (IoV), vehicle wireless communication poses serious cybersecurity challenges. Faulty information, such as fake vehicle positions and speeds sent by surrounding vehicles, could cause vehicle collisions, traffic jams, and even casualties. Additionally, private vehicle data leakages, such as vehicle trajectory and user account information, may damage user property and security. Therefore, achieving a cyberattack-defense scheme in the IoV system with faulty data saturation is necessary. This paper proposes a Federated Learning-based Vehicle Trajectory Prediction Algorithm against Cyberattacks (FL-TP) to address the above problems. The FL-TP is intensively trained and tested using a publicly available Vehicular Reference Misbehavior (VeReMi) dataset with five types of cyberattacks: constant, constant offset, random, random offset, and eventual stop. The results show that the proposed FL-TP algorithm can improve cyberattack detection and trajectory prediction by up to 6.99 % and 54.86%, respectively, under the maximum cyberattack permeability scenarios compared with benchmark methods.