Fourth Quarter 2020
Manuscript Submission Deadline
Call for Papers
With the explosive growth of smart devices and development of wireless technology, numerous new applications such as Augmented Reality (AR), Virtual Reality (VR), Mixed Reality (MR), autonomous driving and intelligent manufactory enter our daily life and put stringent requirements on the current communications technologies. Boosted by multimedia applications and Internet-of-Things (IoT), immersive communications is considered to bring a novel vision on the development of advanced communications systems and networks. Nevertheless, to fully explore the immersive experience and applications, ultra-reliable, low latency and high data rate communications systems are required. However, the conventional access network can hardly accommodate such stringent requirements, due to limited capacity and long latency on the backhaul links. Novel approaches that bring various network functions and contents to the network edge, i.e., mobile edge computing and caching, are promising to tackle the aforementioned challenges.
On the other hand, there are various open issues on improving the capability of mobile edge computing and caching. The addressing of these challenges requires complicated network optimization methods, and often needs online adaptation with respect to the volatile network states, which the conventional model-based optimization can hardly tackle. In this regard, machine learning approaches can provide valuable ingredients and potentially efficient solutions. These are also able to help revisit the elementary wireless network techniques like scheduling and transmission, specifically in the era of wireless edge intelligence. Moreover, cross-discipline research not only means optimizing the wireless communications via machine leaning, but can also reveal how wireless can assist the artificial intelligence (AI)-based applications. This is vital, as AI can be a key traffic contributor in the future, and such a topic has been rarely touched upon in recent studies.
Future natural and effective immersive experiences will be created by drawing upon intertwined research areas including multimedia communications, machine learning, signal processing, computer vision, wireless networking, sensors, displays and sound reproduction systems, where edge Intelligence will play a significant role. This special issue aims to consolidate the current state-of-the art in terms of fundamental research ideas and network engineering, geared towards exploiting wireless edge intelligence for providing immersive communications that requires low latency access to computing resources, such as AR/VR/MR, connected autonomous driving, massive IoT, smart grid, intelligent manufactory, and others.
The topics of interests related to edge intelligence for immersive communications include, but are not limited to:
- System modelling: Computation modelling, content modelling, energy consumption modelling
- Novel transmission technologies for learning-based applications at the network edge
- Scheduling schemes for efficient training, inference for edge learning/edge AI
- Timely data acquisition mechanisms to support delay sensitive edge processing
- Coded computing for edge intelligence
- Enabling technologies, e.g., SDN, NFV, CRAN, D2D, cloud/fog computing and networking
- Emerging applications via edge intelligence: vehicular networking, massive IoT, smart grid, healthcare, intelligent manufactory
- Novel network architecture: convergence of computing, communications and caching, content/information-centric network, cognitive computing and networking, big data analytics
- Context-aware schemes: incentive mechanism for computing and caching, pricing, game theoretic approach, network economics, caching placement and delivery
- Mobility management for mobile edge computing and proactive caching, the way to exploit the mobility for more computing and caching opportunities
- Energy efficiency aspects: energy harvesting, energy storage, energy transfer
- AR/VR applications
- Tactile internet
- Security and privacy issues
- Prototyping, test-beds and field trials
Submit manuscript to: https://mc.manuscriptcentral.com/oj-coms
For information regarding IEEE OJ-COMS including its publication policy and fees, please visit the website https://www.comsoc.org/publications/journals/ieee-ojcoms
Lead Guest Editor
Zheng Chang, University of Jyväskylä, Finland
Xiaojiang Du, Temple University, USA
Zhu Han, University of Houston, USA
Geyong Min, University of Exeter, UK
Zhiwei Zhao, University of Electronic Science and Technology of China, China,
Di Zhang, Huawei, China
For inquiries regarding this Special Issue, please contact the Lead Guest Editor.