User-centric Immersive Communications in 6G: A Data-oriented Approach via Digital Twin

In this article, we present a novel user-centric service provision for immersive communications (IC) in 6G to deal with the uncertainty of individual user behaviors while satisfying unique requirements on the quality of multi-sensory experience. To this end, we propose a data-oriented approach for network resource management, featuring personalized data management that can support network modeling tailored to different user demands. Our approach leverages the digital twin (DT) technique as a key enabler. Particularly, a DT is established for each user, and the data attributes in the DT are customized based on the characteristics of the user. The DT functions, corresponding to various data operations, are customized in the development, evaluation, and update of network models to meet unique user demands. A trace-driven case study demonstrates the effectiveness of our approach in achieving user-centric IC and the significance of personalized data management in 6G.

Meta Learning Based Adaptive Cooperative Perception in Nonstationary Vehicular Networks

To accommodate high network dynamics in real-time cooperative perception (CP), reinforcement learning (RL) based adaptive CP schemes have been proposed, to allow adaptive switchings between CP and stand-alone perception modes among connected and autonomous vehicles. The traditional offline-training online-execution RL framework suffers from performance degradation under nonstationary network conditions. To achieve fast and efficient model adaptation, we formulate a set of Markov decision processes for adaptive CP decisions in each stationary local vehicular network (LVN). A meta RL solution is proposed, which trains a meta RL model that captures the general features among LVNs, thus facilitating fast model adaptation for each LVN with the meta RL model as an initial point. Simulation results show the superiority of meta RL in terms of the convergence speed without reward degradation. The impact of the customization level of meta models on the model adaptation performance has also been evaluated.

Active ML for 6G: Towards Efficient Data Generation, Acquisition, and Annotation

This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active learning framework that considers both annotation (i.e., what is the label) and data acquisition (i.e., which and how many samples to collect). Moreover, we explore the synergy between generative artificial intelligence (AI) and active learning to overcome existing limitations in both active learning and generative AI. This paper also features a case study on a mmWave throughput prediction problem to demonstrate the practical benefits and improved performance of active learning for 6G networks. Furthermore, we discuss how the implications of active learning extend to numerous 6G network use cases. We highlight the potential of active learning based 6G networks to enhance computational efficiency, data annotation and acquisition efficiency, adaptability, and overall network intelligence. We conclude with a discussion on challenges and future research directions for active learning in 6G networks, including development of novel query strategies, distributed learning integration, and inclusion of human- and machine-in-the-loop learning.

Accuracy-Aware Cooperative Sensing and Computing for Connected Autonomous Vehicles

To maintain high perception performance among connected and autonomous vehicles (CAVs), in this paper, we propose an accuracy-aware and resource-efficient raw-level cooperative sensing and computing scheme among CAVs and road-side infrastructure. The scheme enables fined-grained partial raw sensing data selection, transmission, fusion, and processing in per-object granularity, by exploiting the parallelism among object classification subtasks associated with each object. A supervised learning model is trained to capture the relationship between the object classification accuracy and the data quality of selected object sensing data, facilitating accuracy-aware sensing data selection. We formulate an optimization problem for joint sensing data selection, subtask placement and resource allocation among multiple object classification subtasks, to minimize the total resource cost while satisfying the delay and accuracy requirements. A genetic algorithm based iterative solution is proposed for the optimization problem. Simulation results demonstrate the accuracy awareness and resource efficiency achieved by the proposed cooperative sensing and computing scheme, in comparison with benchmark solutions.

Model-Assisted Learning for Adaptive Cooperative Perception of Connected Autonomous Vehicles

Cooperative perception (CP) is a key technology to facilitate consistent and accurate situational awareness for connected and autonomous vehicles (CAVs). To tackle the network resource inefficiency issue in traditional broadcast-based CP, unicast-based CP has been proposed to associate CAV pairs for cooperative perception via vehicle-to-vehicle transmission. In this paper, we investigate unicast-based CP among CAV pairs. With the consideration of dynamic perception workloads and channel conditions due to vehicle mobility and dynamic radio resource availability, we propose an adaptive cooperative perception scheme for CAV pairs in a mixed-traffic autonomous driving scenario with both CAVs and human-driven vehicles. We aim to determine when to switch between cooperative perception and stand-alone perception for each CAV pair, and allocate communication and computing resources to cooperative CAV pairs for maximizing the computing efficiency gain under perception task delay requirements. A model-assisted multi-agent reinforcement learning (MARL) solution is developed, which integrates MARL for an adaptive CAV cooperation decision and an optimization model for communication and computing resource allocation. Simulation results demonstrate the effectiveness of the proposed scheme in achieving high computing efficiency gain, as compared with benchmark schemes.

User Dynamics-Aware Edge Caching and Computing for Mobile Virtual Reality

In this paper, we present a novel content caching and delivery approach for mobile virtual reality (VR) video streaming. The proposed approach aims to maximize VR video streaming performance, i.e., minimizing video frame missing rate, by proactively caching popular VR video chunks and adaptively scheduling computing resources at an edge server based on user and network dynamics. First, we design a scalable content placement scheme for deciding which video chunks to cache at the edge server based on tradeoffs between computing and caching resource consumption. Second, we propose a machine learning-assisted VR video delivery scheme, which allocates computing resources at the edge server to satisfy video delivery requests from multiple VR headsets. A Whittle index-based method is adopted to reduce the video frame missing rate by identifying network and user dynamics with low signaling overhead. Simulation results demonstrate that the proposed approach can significantly improve VR video streaming performance over conventional caching and computing resource scheduling strategies.

AI-Assisted Slicing-Based Resource Management for Two-Tier Radio Access Networks

While network slicing has become a prevalent approach to service differentiation, radio access network (RAN) slicing remains challenging due to the need of substantial adaptivity and flexibility to cope with the highly dynamic network environment in RANs. In this paper, we develop a slicing-based resource management framework for a two-tier RAN to support multiple services with different quality of service (QoS) requirements. The developed framework focuses on base station (BS) service coverage (SC) and interference management for multiple slices, each of which corresponds to a service. New designs are introduced in the spatial, temporal, and slice dimensions to cope with spatiotemporal variations in data traffic, balance adaptivity and overhead of resource management, and enhance flexibility in service differentiation. Based on the proposed framework, an energy efficiency maximization problem is formulated, and an artificial intelligence (AI)-assisted approach is proposed to solve the problem. Specifically, a deep unsupervised learning-assisted algorithm is proposed for searching the optimal SC of the BSs, and an optimization-based analytical solution is found for managing interference among BSs. Simulation results under different data traffic distributions demonstrate that our proposed slicing-based resource management framework, empowered by the AI-assisted approach, outperforms the benchmark frameworks and achieves a close-to-optimal performance in energy efficiency.

Digital Twin-Based 3D Map Management for Edge-Assisted Mobile Augmented Reality

In this paper, we design a 3D map management scheme for edge-assisted mobile augmented reality (MAR) to support the pose estimation of individual MAR device, which uploads camera frames to an edge server. Our objective is to minimize the pose estimation uncertainty of the MAR device by periodically selecting a proper set of camera frames for uploading to update the 3D map. To address the challenges of the dynamic uplink data rate and the time-varying pose of the MAR device, we propose a digital twin (DT)-based approach to 3D map management. First, a DT is created for the MAR device, which emulates 3D map management based on predicting subsequent camera frames. Second, a model-based reinforcement learning (MBRL) algorithm is developed, utilizing the data collected from both the actual and the emulated data to manage the 3D map. With extensive emulated data provided by the DT, the MBRL algorithm can quickly provide an adaptive map management policy in a highly dynamic environment. Simulation results demonstrate that the proposed DT-based 3D map management outperforms benchmark schemes by achieving lower pose estimation uncertainty and higher data efficiency in dynamic environments.

Toward Immersive Communications in 6G

The sixth generation (6G) networks are expected to enable immersive communications and bridge the physical and the virtual worlds. Integrating extended reality, holography, and haptics, immersive communications will revolutionize how people work, entertain, and communicate by enabling lifelike interactions. However, the unprecedented demand for data transmission rate and the stringent requirements on latency and reliability create challenges for 6G networks to support immersive communications. In this survey article, we present the prospect of immersive communications and investigate emerging solutions to the corresponding challenges for 6G. First, we introduce use cases of immersive communications, in the fields of entertainment, education, and healthcare. Second, we present the concepts of immersive communications, including extended reality, haptic communication, and holographic communication, their basic implementation procedures, and their requirements on networks in terms of transmission rate, latency, and reliability. Third, we summarize the potential solutions to addressing the challenges from the aspects of communication, computing, and networking. Finally, we discuss future research directions and conclude this study.

Holistic Network Virtualization and Pervasive Network Intelligence for 6G

In this tutorial paper, we look into the evolution and prospect of network architecture and propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed architecture has two key elements, i.e., holistic network virtualization and pervasive artificial intelligence (AI). The holistic network virtualization consists of network slicing and digital twin, from the aspects of service provision and service demand, respectively, to incorporate service-centric and user-centric networking. The pervasive network intelligence integrates AI into future networks from the perspectives of networking for AI and AI for networking, respectively. Building on holistic network virtualization and pervasive network intelligence, the proposed architecture can facilitate three types of interplay, i.e., the interplay between digital twin and network slicing paradigms, between model-driven and data-driven methods for network management, and between virtualization and AI, to maximize the flexibility, scalability, adaptivity, and intelligence for 6G networks. We also identify challenges and open issues related to the proposed architecture. By providing our vision, we aim to inspire further discussions and developments on the potential architecture of 6G.