As wireless communication becomes ubiquitous, a excess of devices ranging from smart phones to medical implants is increasingly competing for use of unlicensed spectrum. As per today's network, these radio devices may interfere with each other by limiting its real-world throughput and capacity. To overcome the hurdles and challenge, these devices must calculate the nature of the environment; define interference limits, and to predict future time samples to cancel unwanted components of a corrupted received signal. If not accounted for, interference can lead to corruption of data that leads to unnecessary retransmission of corrupted packets. Since these results in a degradation of network performance, solving the problem of wireless interference prediction will yield networks with robust performance even while multiple devices contend for limited, shared spectrum. Solving the wireless interference problem is challenging because a wireless signal can be corrupted by a variety of other ambient wireless signals, such as Bluetooth, Wi-Fi, or Long Term Evolution protocol transmissions. The environment, such as walls and obstacles, can further attenuate these signals. The net randomness can make determining wireless interference a challenging endeavor. At the same time, wireless signals may exhibit specific periodic structure and cyclo-stationary features. Modern wireless architectures have not fully realized the potential of utilizing this data to segment and predict wireless interference. The proliferation of wireless devices ranging from Smartphone to medical implants has led to unprecedented levels of interference in shared, unlicensed spectrum. Modern devices face the challenging task of estimating such interfering signals from received transmissions in order to effectively cancel interference in analog circuitry. In an effort to solve this problem, the usage of deep learning to segment corrupted wireless transmissions into a desired signal of interest and interference estimate.
This special issue is an ideal platform for the researchers to come out with innovative ideas and approaches to contribute this area of research. This issue gains much importance since it directly affects efficiency of personal communication systems.
Topics include, but are not limited to, the following:
- Interference correction by deep machine learning.
- Challenges and hurdles of wireless personal communication and its solution by machine learning methods.
- Deep Belief Networks and its application in Wireless personal communications.
- Transcend methods of implementations optimized M2M.
- Machine learning for wireless communication interference, etc.,
- Convolution Neural networks and its applications in wireless communications.
- Deep learning for ADHOC and its strategies.
- Deep learning methods for Wireless Personal Communication Networks.
- Autonomic and Evolutionary Communication
- Bio-Inspired and Adaptive Gaming Applications
- AI Security, Trust, Assurance, and Resilience in mobile platforms
- Collective Intelligence in AI
- Collision-based Intelligence in Virtual Communication
- Context-aware Networking
- Data aggregation and fusion of AI
- Energy-efficient AI Monitoring
- Machine Intelligence and Virtual Reality
- AI and Haptic Technology
- AI Technology and Graphics Processing
- Augmented Reality and Cyber Space
Submission deadline: 30 June 2021
Notification to Authors: 10 August 2021
Final Versions Due: 25 October 2021
Lead Guest Editor
B.Nagaraj, Dean - Innovation Centre, Rathinam Group of Institutions, Coimbatore, Tamilnadu, India
Danilo Pelusi, University of Teramo, Italy
Raffaele Mascella, University of Teramo, Italy
Yong Deng, Institute of Fundamental and Frontier Science, University of Electronic Science and Technology of China
Authors should prepare their manuscript according to the guide for authors available from the online submission page of the EURASIP Journal on Wireless Communications and Networking at https://jwcn-eurasipjournals.springeropen.com/. Authors should select "AI enabled Virtual - Wireless Personal Communications: Recent Challenges and its Avenues" when they reach the “Article Type” step in the submission process.
Submitted papers must contain original work, which has neither been previously published nor it is currently under review by another journal or conference. Previously published or accepted conference papers must contain at least 50% new material to be considered for the special issue.
All papers will be peer-reviewed by at least two independent reviewers. Requests for additional information should be addressed to the corresponding guest editor.
Submissions will also benefit from the usual advantages of open access publication:
- Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
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- Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed
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