IEEE Future Networks Tech Focus
Issue 16, June 2023
In This Issue
- Congestion Management Techniques for Future Satellite Networks
- TDD Mode on NTN Direct to Satellite Service
- Reference Architectures for Enabling Integrated Satellite-6G Applications and Services
- QoS Optimization-based Core Network Distribution Strategy For B5G Non-terrestrial-network (NTN)
- Distributed Deep Reinforcement Learning for Latency Optimized Computation Offloading in Aerial-Assisted MEC Networks
Authors: Pablo Madoery, Carleton University; Gunes Karabulut Kurt, Polytechnique de Montreal, Canada; Halim Yanikomeroglu, Carleton University; Peng Hu, National Research Council, Canada; Khaled Ahmed, Satellite Systems, MDA, Canada; Guillaume Lamontagne, Satellite Systems, MDA, Canada
Future satellite networks will make intensive use of inter-satellite links and computing edge capabilities to conform communication networks in space that connect millions of users on the ground. Unlike terrestrial Internet-based networks, these new satellite networks will need to incorporate novel techniques to avoid or mitigate congestion. In this paper we describe the main features of schemes incorporating these techniques and compare their performance by means of simulations of a realistic satellite constellation. The results show the benefits of moving from reactive feedback-based schemes to proactive schemes that prevent congestion before it occurs..
Authors: Shaoli Kang, China Information Communication Technology Group, China; Deshan Miao, China Information Communication Technology Group, China; Shaohui Sun, China Information Communication Technology Group, China; Shanzhi Chen, China Information Communication Technology Group, China
Though the Time Division Duplex (TDD) is a typical mode in the terrestrial communication, it was seldom applied in the satellite communication. With the increasing requirements on Direct to Satellite Service (D2SS), TDD is expected to solve the problems of low frequency shortages suitable for handheld terminals. This paper tries to discuss TDD mode on Non-Terrestrial Network (NTN) to support D2SS, including advantages and disadvantages, technical challenges and also potential solutions for TDD mode.
Authors: Debrabata Dalai, Indian Institute of Space Science and Technology; Sarath Babu, Iowa State University, Ames, USA; BS Manoj, Indian Institute of Space Science and Technology
Reference Architectures (RAs) play an important role in the integration of 6G terrestrial and satellite networks. In this paper, we present the essence of the reference architectural roadmap as per the Edition-3 document of the Satellite Working Group of IEEE Future Networks Initiative (FNI). We focus on an integrated virtualized 6G-satellite architecture. Further, we present one of the case studies, Space Based Hosting Service (SBHS) approach with simulation results. SBHS approach deploys the content-servers in LEO satellite to achieve the low-latency service requirements. The architecture of SBHS is a special case over Reference Architecture-3. We achieved minimum average end-to-end latency of 7.75ms for the geographical area covering India by the proposed SBHS approach.
Authors: Yifei Jiang, Shanghai Jiao Tong University; Zhili Sun, University of Surrey; Shufan Wu, Shanghai Jiao Tong University
Since the initial commercial deployment of 5G in 2019, the research community and industry have already started to outline their future 6G visions especially into the direction of blending the physical and the virtual worlds in the digitised society. While 5G will continue to evolve in the near future, there are also brand new technical challenges that will be mainly tackled in the context of 6G, e.g. ambient sensing, precision localisation and synchronisation, and manipulation of the radio propagation environment. In this article we highlight our 6G vision and some selected 6G-oriented research activities carried out at the 6G Innovation Centre (6GIC) of the University of Surrey. 6GIC will be a key UK-based hub for global innovation and collaboration on 6G wireless, involving governments, regulators, mobile operators, vendors, enterprises, and leading research and development centres, as 5GIC was for 5G innovation at the University. In this article we first outline some key features of our top-level 6G vision before diving into specific topics where a number of selected research items investigated at 6GIC are introduced.
Distributed Deep Reinforcement Learning for Latency Optimized Computation Offloading in Aerial-Assisted MEC Networks
Authors: Nida Fatima and Paresh Saxena; Department of Computer Science and Information Systems, BITS Pilani, India; Giovanni Giambene, Department of Information Engineering and Mathematics, University of Siena, Italy
The ultra-low latency requirements of mission-critical applications necessitate high computation power. To accomplish this objective, multi-access edge computing (MEC) is a crucial technology that brings computation resources closer to user equipments (UE) and provides an offloading option. In situations where terrestrial MEC cannot meet the requirements, aerial-assisted MEC with unmanned aerial vehicles (UAVs) can be utilized due to their flexible deployment and enhanced coverage. However, for a latency-optimized optimal offloading strategy, it is essential to consider the challenges posed by the environment's dynamics, the availability of resources at UAVs, and the computation requirements of UEs. To address these challenges, we present four distributed deep reinforcement learning (DRL) frameworks for efficient computation offloading in aerial-assisted MEC networks.
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