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

 

 

 

  

QoS Optimization-based Core Network Distribution Strategy For B5G Non-terrestrial-network (NTN)

June 2023

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.

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Distributed Deep Reinforcement Learning for Latency Optimized Computation Offloading in Aerial-Assisted MEC Networks

June 2023

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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