In this paper, we propose a novel mobility-aware resource provisioning scheme for edge-assisted extended reality (XR) services. The goal is to minimize resource consumption while satisfying user quality of experience (QoE) requirement, which is measured by the weighted sum of visual quality, quality variation, and round-trip interaction latency. Specifically, we present a mobility model to capture both user spatial movements and XR content interaction features. Since user viewing distance and interaction time are key model parameters that affect the spatiotemporal service demand for XR content rendering and delivery at the edge, we estimate user-specific model parameters and adopt a sample average approximation method to model the relationship between user QoE and the consumption of both communication and edge computing resources. We design a coordinate descent algorithm to make resource provisioning decisions, where a deep neural network provides a valuable initial point to accelerate convergence. Simulation results demonstrate that our proposed scheme is more efficient to utilize network resources in comparison with benchmark schemes while satisfying user QoE requirements.

In this paper, we investigate secure and efficient computation offloading in mobile edge computing (MEC) systems. Particularly, computation tasks are dynamically partitioned into multiple sub-tasks for parallel processing on both local devices and edge servers to reduce service latency. In addition, the friendly jamming technique is applied to degrade the interception capabilities of eavesdroppers to protect data secrecy. Our objective is to maximize the number of tasks completed before their respective deadlines and, at the same time, to minimize energy consumption and service latency with security guarantee. To simultaneously handle heterogeneous offloading decisions, we propose an omni-deep deterministic policy gradient (Omni-DDPG) approach that integrates a variational autoencoder for discrete jammer selection, an Ornstein-Uhlenbeck process for continuous computing power allocation, and a Dirichlet distribution for constrained continuous task partitioning. The proposed approach has a low complexity growing linearly with the system size, maintains light memory usage, and enables real-time decisions without using complicated optimization solvers. Extensive simulation results demonstrate our proposed approach can achieve better performance in terms of the number of completed tasks before expiration, energy consumption, and service latency, while satisfying secrecy requirements, compared with the benchmarks such as DDPG, deep Q-network, and greedy algorithms.

Future 6G networks are envisioned to facilitate edge-assisted mobile augmented reality (MAR) via strengthening the collaboration between MAR devices and edge servers. In order to provide immersive user experiences, MAR devices must timely upload camera frames to an edge server for simultaneous localization and mapping (SLAM)-based device pose tracking. In this paper, to cope with user-specific and non-stationary uplink data traffic, we develop a digital twin (DT)-based approach for user-centric communication service provision for MAR. Specifically, to establish DTs for individual MAR devices, we first construct a data model customized for MAR that captures the intricate impact of the SLAM-based frame uploading mechanism on the user-specific data traffic pattern. We then define two DT operation functions that cooperatively enable adaptive switching between different data-driven models for capturing non-stationary data traffic. Leveraging the user-oriented data management introduced by DTs, we propose an algorithm for network resource management that ensures the timeliness of frame uploading and the robustness against inherent inaccuracies in data traffic modeling for individual MAR devices. Trace-driven simulation results demonstrate that the user-centric communication service provision achieves a 14.2% increase in meeting the camera frame uploading delay requirement in comparison with the slicing-based communication service provision widely used for 5G.

In this paper, we propose a novel service continuityaware Service Function Chain (SFC) embedding scheme for dynamic large-scale LEO satellite networks, where service disruptions occur when satellites hosting virtual network functions of an SFC move out of the service region. Particularly, we define a new metric, i.e., the Remaining Time to Migration (RTTM), which indicates the remaining functional time of an SFC before SFC reconfiguration is needed. We then formulate a service continuity-aware SFC embedding problem with the objective of maximizing the long-term acceptance ratio while increasing the normalized RTTM of accepted SFCs. We propose a scalable graph neural network-assisted deep reinforcement learning (DRL) approach to solve the embedding problem. By employing a differentiable pooling technique, we condense the feature representation of large-scale LEO satellite networks, thereby reducing the computational complexity of the down-stream DRL-based decision-making. Simulation results show that our approach reduces the proportion of reconfigured SFCs by 60 % compared to the benchmark, indicating its effectiveness in enhancing service continuity.

This paper presents a novel model-assisted learning scheme for resource allocation in environment-aware mobile augmented reality (AR) content delivery. The goal is to minimize the long-term communication resource consumption for delivering virtual content visible to an individual AR user by optimizing the communication resource allocation for user positioning and environment mapping. In specific, we first develop a mathematical model to estimate the content visibility uncertainty and the content delivery resource consumption. We then generate a reference resource allocation decision that guides a deep reinforcement learning-based decision process to efficiently adapt to non-stationary user and environment dynamics. We conduct trace-driven simulations to evaluate the performance of the proposed scheme, and the results demonstrate that, the proposed scheme significantly reduces communication resource consumption for delivering virtual content visible to an individual AR user, compared to benchmark schemes.

Resource slicing in low Earth orbit satellite networks (LSN) is essential to support diversified services. In this paper, we investigate a resource slicing problem in LSN to reserve resources in satellites to achieve efficient resource provisioning. To address the challenges of non-stationary service demands, inaccurate prediction, and satellite mobility, we propose an adaptive digital twin (DT)-assisted resource slicing scheme for robust and adaptive resource management in LSN. Specifically, a slice DT, being able to capture the service demand prediction uncertainty through collected service demand data, is constructed to enhance the robustness of resource slicing decisions for dynamic service demands. In addition, the constructed DT can emulate resource slicing decisions for evaluating their performance, enabling adaptive slicing decision updates to efficiently reserve resources in LSN. Simulation results demonstrate that the proposed scheme outperforms benchmark methods, achieving low service demand violations with efficient resource consumption.

In this paper, we propose a digital twin (DT)-based user-centric approach for processing sensing data in an integrated sensing and communication (ISAC) system. The considered scenario involves an ISAC device with a lightweight deep neural network (DNN) and a mobile edge computing (MEC) server with a large DNN. After collecting sensing data, the ISAC device either processes the data locally or uploads them to the server for higher-accuracy data processing. To cope with data drifts, the server updates the lightweight DNN when necessary, referred to as continual learning. Our objective is to minimize the long-term average computation cost of the MEC server by jointly optimizing two decisions, i.e., sensing data offloading and sensing data selection for the DNN update. A DT of the ISAC device is constructed to predict the impact of potential decisions on the long-term computation cost of the server, based on which the decisions are made with closed-form formulas. Experiments on executing DNN-based human motion recognition tasks are conducted to demonstrate the outstanding performance of the proposed DT-based approach in computation cost minimization.

Device-edge collaboration on deep neural network (DNN) inference is a promising approach to efficiently utilizing network resources for supporting Artificial Intelligence of Things (AIoT) applications. In this article, we propose a novel digital twin (DT)-assisted approach to device-edge collaboration on DNN inference that determines whether and when to stop local inference at a device and upload the intermediate results to complete the inference on an edge server. Instead of determining the collaboration for each DNN inference task only upon its generation, multi-step decision making is performed during the on-device inference to adapt to the dynamic computing workload status at the device and the edge server. To enhance the adaptivity, a DT is constructed to evaluate all potential offloading decisions for each DNN inference task, which provides augmented training data for a machine learning-assisted decision-making algorithm. Then, another DT is constructed to estimate the inference status at the device to avoid frequently fetching the status information from the device, thus reducing the signaling overhead. We also derive necessary conditions for optimal offloading decisions to reduce the offloading decision space. Simulation results demonstrate the outstanding performance of our DT-assisted approach in terms of balancing the tradeoff among inference accuracy, delay, and energy consumption.

Multicast short video streaming can enhance bandwidth utilization by enabling simultaneous video transmission to multiple users over shared wireless channels. The existing network management schemes mainly rely on the sequential buffering principle and general quality of experience (QoE) model, which may deteriorate QoE when users’ swipe behaviors exhibit distinct spatiotemporal variation. In this paper, we propose a digital twin (DT)-based network management scheme to enhance QoE. Firstly, user status emulated by the DT is utilized to estimate the transmission capabilities and watching probability distributions of sub-multicast groups (SMGs) for an adaptive segment buffering. The SMGs’ buffers are aligned to the unique virtual buffers managed by the DT for a fine-grained buffer update. Then, a multicast QoE model consisting of rebuffering time, video quality, and quality variation is developed, by considering the mutual influence of segment buffering among SMGs. Finally, a joint optimization problem of segment version selection and slot division is formulated to maximize QoE. To efficiently solve the problem, a data-model-driven algorithm is proposed by integrating a convex optimization method and a deep reinforcement learning algorithm. Simulation results based on the real-world dataset demonstrate that the proposed DT-based network management scheme outperforms benchmark schemes in terms of QoE improvement.

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

In this paper, we investigate digital twin (DT)-assisted adaptive deep neural network (DNN) inference in the Industrial Internet of Things (IIoT). We consider a scenario that an edge server has a full-size DNN for high-accuracy inference, while an IIoT device has a lightweight DNN for fast on-device inference. The IIoT device generates computing tasks, such as object recognition, to be processed by DNN. For each task, a local controller at the network edge determines whether or not to offload the task to the edge server before it enters each layer of the lightweight DNN. The objective is to find the task offloading point that maximizes a utility including delay, inference accuracy, and on-device energy consumption. To achieve this objective, we propose an online DT-assisted task offloading scheme, which exploits DTs to capture the task processing status at the IIoT device and the workload at the edge server. Simulation results demonstrate the excellent performance of the proposed adaptive DT-assisted DNN inference on delay, inference accuracy, and on-device energy consumption.

Traditionally, dynamic spectrum access (DSA) based on cooperative spectrum sensing relies on a centralized fusion centre to fuse and store the sensing results, which is vulnerable to single point of failure. In this paper, we propose a sensing-based DSA framework which is enabled by blockchain. The proposed DSA framework includes a protocol that specifies a time-slotted-based five-phase operations. In the proposed framework, each secondary user (SU) acts as both a sensing node for cooperatively sensing the spectrum and a node, i.e., a miner and a verifier, in the blockchain network for mining and updating the sensing and access results in a distributed and secure manner without the need for a fusion centre. In order to incentivize SUs for participating in such energy-consuming operations of the blockchain network, we reward them with tokens for sensing and mining, which can be used to bid for the access to the spectrum opportunities. The sensing and mining policies which they use to determine when to sense and mine affect the number of tokens they can obtain and subsequently how they bid for the spectrum. Hence, the performance of the system depends on their sensing-access-mining policy. Therefore, we consider a heuristic sensing-access-mining policy that determines whether to participate in sensing and mining in a probabilistic manner and that determines how much to bid based on its buffer occupancy and the number of available tokens. Simulation results show that although increasing sensing and mining probabilities can increase average transmission rate, it also leads to higher energy consumption. Moreover, there exists an optimal set of sensing and mining probabilities that maximize the system energy efficiency.

Modulation classification using deep neural networks has recently received increasing attention due to its capability in learning rich features of data. In this paper, we propose a low- complexity blind data-driven modulation classifier. Our classifier operates robustly over Rayleigh fading channels under uncertain noise conditions modeled using a mixture of three types of noise, namely, white Gaussian noise, white non- Gaussian noise and correlated non-Gaussian noise. The proposed classifier consists of several layers of recurrent neural networks (RNN) which is well-suited for learning representations from time-correlated data. The classifier is trained using the labeled raw signal samples generated under different noise conditions. Simulation results show that the performance of our proposed classifier approaches that of maximum likelihood classifiers with perfect channel knowledge and outperforms existing expectation maximum (EM) and expectation conditional maximum (ECM) classifiers which iteratively estimate channel and noise parameters.