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- research-articleJanuary 2024
MLOps in the Metaverse: Human-Centric Continuous Integration
IEEE Journal on Selected Areas in Communications (JSAC), Volume 42, Issue 3March 2024, Pages 737–751https://doi.org/10.1109/JSAC.2023.3345385The metaverse is a virtual world that exists entirely in a computer-generated environment, and it offers a new frontier for machine learning. One of the major challenges for using machine learning in the metaverse is MLOps (Machine Learning Operations), ...
- research-articleDecember 2023
Spatio-Temporal Identity Multi-Graph Convolutional Network for Traffic Prediction in the Metaverse
IEEE Journal on Selected Areas in Communications (JSAC), Volume 42, Issue 3March 2024, Pages 669–679https://doi.org/10.1109/JSAC.2023.3345389The metaverse is at the forefront of the next-generation internet application, where billions of users seamlessly immerse themselves in a hybrid reality of physical-virtual worlds and switch between virtual environments thanks to reliable resource ...
- research-articleDecember 2023
AIRIC: Orchestration of Virtualized Radio Access Networks With Noisy Neighbours
IEEE Journal on Selected Areas in Communications (JSAC), Volume 42, Issue 2Feb. 2024, Pages 432–445https://doi.org/10.1109/JSAC.2023.3339749Radio Access Networks virtualization (vRAN) is on its way becoming a reality driven by the new requirements in mobile networks, such as scalability and cost reduction. Unfortunately, there is no free lunch but a high price to be paid in terms of computing ...
- research-articleNovember 2023
Safe and Accelerated Deep Reinforcement Learning-Based O-RAN Slicing: A Hybrid Transfer Learning Approach
IEEE Journal on Selected Areas in Communications (JSAC), Volume 42, Issue 2Feb. 2024, Pages 310–325https://doi.org/10.1109/JSAC.2023.3336191The open radio access network (O-RAN) architecture supports intelligent network control algorithms as one of its core capabilities. Data-driven applications incorporate such algorithms to optimize radio access network (RAN) functions via RAN intelligent ...
- research-articleOctober 2023
Networked Sensing With AI-Empowered Interference Management: Exploiting Macro-Diversity and Array Gain in Perceptive Mobile Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 12Dec. 2023, Pages 3863–3877https://doi.org/10.1109/JSAC.2023.3322828Sensing will become an important service of future wireless networks to assist innovative applications such as autonomous driving and environment monitoring. Perceptive mobile networks (PMNs) were proposed to incorporate sensing capability into current ...
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- research-articleOctober 2023
ToA and TDoA Estimation Using Artificial Neural Networks for High-Accuracy Ranging
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 12Dec. 2023, Pages 3816–3830https://doi.org/10.1109/JSAC.2023.3322803High-accuracy positioning enables various applications such as industrial asset tracking, autonomous driving and process automation. Accurate location information relies on accurate time-of-arrival (ToA) or time-difference-of-arrival (TDoA) estimation in ...
- research-articleOctober 2023
Deep Learning-Based Multi-User Positioning in Wireless FDMA Cellular Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 12Dec. 2023, Pages 3848–3862https://doi.org/10.1109/JSAC.2023.3322799In Cooperative Intelligent Transportation Systems (C-ITS) and Connected Automated Vehicles (CAV), accessing multiple users and providing high-precision positioning are both vital. This paper aims to design an efficient deep learning approach to extend ...
- research-articleOctober 2023
Cooperative Deep-Learning Positioning in mmWave 5G-Advanced Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 12Dec. 2023, Pages 3799–3815https://doi.org/10.1109/JSAC.2023.3322795In application verticals that rely on mission-critical control, such as cooperative intelligent transport systems (C-ITS), 5G-Advanced networks must be able to provide dynamic positioning with accuracy down to the centimeter level. To achieve this level ...
- research-articleOctober 2023
MetaLoc: Learning to Learn Wireless Localization
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 12Dec. 2023, Pages 3831–3847https://doi.org/10.1109/JSAC.2023.3322766Existing localization methods that intensively leverage the environment-specific received signal strength (RSS) or channel state information (CSI) of wireless signals are rather accurate in certain environments. However, these methods, whether based on ...
- research-articleSeptember 2023
Cooperative Multi-Agent Deep Reinforcement Learning for Computation Offloading in Digital Twin Satellite Edge Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 11Nov. 2023, Pages 3414–3429https://doi.org/10.1109/JSAC.2023.3313595With the development of commercial off-the-shelf hardware, low Earth orbit (LEO) satellites are promising to provide flexible edge computing services. In this paper, we investigate a digital twin (DT)-empowered satellite-terrestrial cooperative edge ...
- research-articleAugust 2023
A Masked One-Dimensional Convolutional Autoencoder for Bearing Fault Diagnosis Based on Digital Twin Enabled Industrial Internet of Things
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 10Oct. 2023, Pages 3242–3253https://doi.org/10.1109/JSAC.2023.3310098Bearings are the core component of mechanical equipment. The health status of bearings is the key to the stable operation of the system. Bearing fault diagnosis model can discover damaged bearings in time, which has a large economic value for enterprises. ...
- research-articleAugust 2023
Digital Twins Based Intelligent State Prediction Method for Maneuvering-Target Tracking
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 11Nov. 2023, Pages 3589–3606https://doi.org/10.1109/JSAC.2023.3310109Maneuvering-target tracking has always been an important and challenge work because the unknown and changeable motion-models can easily lead to the failure of model-driven target tracking. Recently, many neural network methods are proposed to improve the ...
- research-articleAugust 2023
Digital Twin Driven Service Self-Healing With Graph Neural Networks in 6G Edge Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 11Nov. 2023, Pages 3607–3623https://doi.org/10.1109/JSAC.2023.33100636G edge networks strive to offer ubiquitous intelligent services, requiring a greater emphasis on network stability and reliability. However, current networks present a low automation degree of the operation, administration and maintenance process. ...
- research-articleAugust 2023
Traffic Prediction-Assisted Federated Deep Reinforcement Learning for Service Migration in Digital Twins-Enabled MEC Networks
IEEE Journal on Selected Areas in Communications (JSAC), Volume 41, Issue 10Oct. 2023, Pages 3212–3229https://doi.org/10.1109/JSAC.2023.3310047In Mobile Edge Computing (MEC) networks, dynamic service migration can support service continuity and reduce user-perceived delay. However, service migration in MEC networks faces significant challenges due to the uncertainty in future traffic demands, ...
- research-articleJune 2022
Active Sensing for Communications by Learning
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 6June 2022, Pages 1780–1794https://doi.org/10.1109/JSAC.2022.3155496This paper proposes a deep learning approach to a class of active sensing problems in wireless communications in which an agent sequentially interacts with an environment over a predetermined number of time frames to gather information in order to perform ...
- research-articleFebruary 2022
Following the Correct Direction: Renovating Sparsified SGD Towards Global Optimization in Distributed Edge Learning
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 2Feb. 2022, Pages 499–514https://doi.org/10.1109/JSAC.2021.3118396Distributed edge learning collaborates powerful edge devices to train a shared global model. Since the frequent communication between the server and workers is very expensive, it is desired to accelerate the learning process. The gradient sparsification ...
- opinionJanuary 2022
Series Editorial The Fourth Issue of the Series on Machine Learning in Communications and Networks
- Geoffrey Y. Li,
- Walid Saad,
- Ayfer Ozgur,
- Peter Kairouz,
- Zhijin Qin,
- Jakob Hoydis,
- Zhu Han,
- Deniz Gunduz,
- Jaafar Elmirghani
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 1Jan. 2022, Pages 1–4https://doi.org/10.1109/JSAC.2021.3126188The third call for papers of the Series on Machine Learning in Communications and Networks has continued to receive a great number of high-quality papers covering various aspects of intelligent communications, from which we have included 26 original ...
- research-articleJanuary 2022
Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 1Jan. 2022, Pages 82–96https://doi.org/10.1109/JSAC.2021.3126088Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is ...
- research-articleJanuary 2022
Learning Task-Oriented Communication for Edge Inference: An Information Bottleneck Approach
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 1Jan. 2022, Pages 197–211https://doi.org/10.1109/JSAC.2021.3126087This paper investigates task-oriented communication for edge inference, where a low-end edge device transmits the extracted feature vector of a local data sample to a powerful edge server for processing. It is critical to encode the data into an <italic>...
- research-articleJanuary 2022
Enabling Robust DRL-Driven Networking Systems via Teacher-Student Learning
IEEE Journal on Selected Areas in Communications (JSAC), Volume 40, Issue 1Jan. 2022, Pages 376–392https://doi.org/10.1109/JSAC.2021.3126085The past few years have witnessed a surge of interest towards deep reinforcement learning (DRL) in computer networks. With extraordinary ability of feature extraction, DRL has the potential to re-engineer the fundamental resource allocation problems in ...