Special Issue Information
Next-generation networks and computing systems aim to provide pervasive, extremely low-latency, ultra-reliable, and trustworthy communication and computation services for diverse use cases and services, including augmented and virtual reality (AR/VR), autonomous driving, vehicle-to-everything (V2X), internet of things (IoT), smart grids, industry 4.0 and precise agriculture. The exponential expansion of devices (e.g., phones, sensors, and vehicles), infrastructures (cellular and WiFi), and services extremely complicate the control and management of the system, which demands new approaches and methodologies.
The advances in machine learning in recent years, e.g., deep neural networks, deep learning, and deep reinforcement learning, show great potential for resolving the challenges under high-dimensional states and network dynamics. Machine learning can enable intelligent wireless communication (e.g., waveform, coding, and receivers), networking (e.g., congestion control and load balancing), and computing (e.g., scheduling and resource provision) in a broad range of scenarios. Nevertheless, the application of machine learning in next-generation networks and computing systems still needs further investigations in terms of robustness, scalability, availability, explainability, transformability, adaptability, and so on.
This Special Issue will focus on machine learning solutions to address the problems in next-generation networks and computing systems. The topics of interest include but are not limited to machine learning, especially deep learning and deep reinforcement learning, for resource management, congestion control, routing, signal processing, computation offloading, joint communication and sensing, and traffic modeling.
This Special Issue, “Machine Learning for Next-Generation Wireless Networks and Computing Systems”, will solicit research papers on various disciplines, including but not limited to the following:
- machine learning for network resource management;
- machine learning for physical layer wireless communication;
- machine learning for transport-layer congestion control;
- machine learning for computation offloading;
- machine learning for traffic engineering;
- machine learning for reconfigurable intelligent surface design;
- machine learning for distributed resource provisioning;
- machine learning for network slicing;
- machine learning for quality-of-service (QoS) assurance;
- machine learning for emerging applications, e.g., autonomous driving, augmented reality, and unmanned aerial vehicles;
- machine learning for emerging scenarios, e.g., edge computing, precise agriculture, and smart cities.
Dr. Qiang Liu
Dr. Tao Han
Dr. Haoxin Wang
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI’s English editing service prior to publication or during author revisions.
- machine learning
- deep learning
- deep reinforcement learning
- resource management
- congestion control
- wireless communication
- edge computing