Machine Learning for Communications and Networks
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: closed (30 May 2021) | Viewed by 5461
Special Issue Editor
Interests: optical wireless communications; visible light communications (VLC) and LiFi networks; heterogenous radio-optical systems; backscatter communication; machine learning for communications and networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleague,
The capabilities of current communication systems cannot meet the demand of the envisioned high degree of heterogeneity in terms of device classes and services. Such diverse services include real-time autonomous machines, safety-critical health applications, and augmented/virtual reality. For instance, wireless deployments are becoming increasingly dense, hierarchical, and heterogeneous to meet the demanding requirements of future services. Beyond the need for higher data-rates, next-generation wireless networks will have to deliver other requirements including more reliability, lower latency, and better security. They also have to be adaptive in real-time to the diverse quality-of-service (QoS) of different devices, while recovering a distorted communication signal in the presence of temporal dispersion, non-linear distortions, or interference and jamming artifacts.
So far, the classical model-based approaches to deal with these challenges and requirements include a portfolio of connectivity solutions based on either standardized or proprietary technologies that have different types of transmission techniques, medium access protocols, and spectrum. Accordingly, all these requirements mandate a fundamental change in the way in which future networks are designed, optimized, and operated.
Machine learning (ML) is emerging as a disruptive technique and architectural framework to intelligently manage the growing complexity and scale of future communication systems, and to meet the requisite QoS of future applications. ML techniques have been applied to all layers of the protocol stack. ML approaches that integrate domain knowledge offer interpretable results, hold promise in addressing the aforementioned challenges. Deep learning has become a prominent and rapidly growing research topic within the field ML for communications and networks. The design of an end-to-end solution, where the whole communication model including the channel can be learned is an intriguing approach. It is therefore natural to extend the investigations to the broader field of ML for communication, sensing, and security & privacy with its strong applications in a wide range of applications in 5G/6G, vehicular, AR/VR, IoT, and Tactile Internet among others.
This special issue (SI) targets broad-based research on ML techniques for communications and networks, towards a new system and architecture design to serve future applications. It seeks to provide a platform for the dissemination of original and unpublished fundamental and applied research results as well as experimental demonstrations. The SI will host contributed papers and one invited paper. Extended papers derived from previously published conference papers will be accepted. A limited number of survey-type papers will be accepted.
We encourage the joint publication of datasets and source code required to reproduce the work by others. We invite authors to embrace widely used tools such as GitHub and/or GitLab for hosting their verifiable source code, baselines, and implementations.
Below, we provide a non-exhaustive list of possible topics. We do not restrict the type of machine learning techniques.
- Design and optimization of modulation and coding schemes
- Channel estimation, prediction, and modeling
- Signal processing
- Transceiver design
- Internet of things and massive connectivity
- Ultra-reliable and low latency communications
- Massive MIMO
- Optical communications
- Large Intelligent Surfaces
- Heterogeneous Networks
- Resource management & optimization
- Self-organized networks and network optimization
- Low-complexity algorithmic/hardware implementation
- Distributed learning approaches
- Joint communication, sensing, and security
- Front-haul and back-haul
Dr. Hany Elgala
Guest Editor
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Keywords
- Machine learning for communications
- Machine learning for networks
- Internet of Things
- 6G networks
- Next generation networks
- Data-driven communications
- Neural networks in communications
- Anomaly detection in communication networks
- Emerging communication systems and applications
- Heterogenous networks
- Distributed/federated learning and communications
- Secure machine learning over communication networks
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