Special Issue "Machine Learning for Communications"
Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 6641
Interests: machine learning; communication networks; cloud computing; information and coding theory; autonomous transportation
Due to the proliferation of applications and services that run over communication networks—ranging from video streaming and data analytics to robotics and augmented reality—tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements. This calls for more powerful, intelligent methods to enable novel network design, deployment, and management. To realize this vision, there is an increasing need to leverage recent developments in machine learning (ML), as well as other artificial intelligence (AI) techniques, and fully integrate them into the design and optimization of communication networks.
There has been a growing number of recent proposals, which aim to harness ML to address various research problems, including traffic engineering, wireless optimization, congestion control, resource management, routing, transport protocol design, content distribution and caching, and user experience optimization. ML techniques allow future communication networks to exploit big data analytics and experience-driven decision making to enable more efficient, autonomous, and intelligent networks. As real-world communication systems are becoming more complex, there are many situations that a single learning agent or a monolithic system is not able to cope with, mandating the use of multiagent learning to yield the best results.
This Special Issue will focus on ML solutions to address problems in communication networks, cutting across various network layers, protocols, applications, and artifacts. Novel algorithms and analyses of learning-based approaches with applications to communication networks are highly encouraged. This Special Issue will accept unpublished original papers and comprehensive reviews focused (but not restricted) on the following research areas:
- Traffic engineering;
- Routing algorithms;
- Congestion control;
- Communication network resource management;
- Network optimization;
- Software-defined networks;
- Content distribution networks;
- Cloud and edge computing;
- Self-driving networks;
- Network security;
- Crowdsourcing/sensing systems;
Dr. Vaneet Aggarwal
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. Entropy is an international peer-reviewed open access monthly 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 1800 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
- reinforcement learning
- multiple agents
- communication networks
- network optimization