With the advent of 5G, the development of extended reality (XR) technology, which combines augmented reality (AR), virtual reality (VR), and advanced human-computer interaction (HCI) technology, is considered one of the key technologies of future metaverse engineering. Especially, XR real-time modeling and simulation (MS) devices that can be applied to various fields (e.g., emergency training simulations, etc.) have tasks with large amounts of data to be processed. However, if the XR task is processed only by wireless user equipment (UE), the UE's energy may be quickly depleted, and the quality of service (QoS) may not be satisfied. To solve these problems, this paper proposes a partial offloading optimization scheme through multiple access edge computing (MEC). In addition, deep reinforcement learning (DRL) is used to reflect the dynamic state of the MEC system and to minimize the delay. The simulation results show that the proposed scheme optimizes the delay performance by efficiently offloading the XR tasks.
|Title of host publication||2022 IEEE International Conference on Consumer Electronics, ICCE 2022|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Publication status||Published - 2022|
|Event||2022 IEEE International Conference on Consumer Electronics, ICCE 2022 - Virtual, Online, United States|
Duration: 2022 Jan 7 → 2022 Jan 9
|Name||Digest of Technical Papers - IEEE International Conference on Consumer Electronics|
|Conference||2022 IEEE International Conference on Consumer Electronics, ICCE 2022|
|Period||22/1/7 → 22/1/9|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT This research was supported by the Ministry of Science and ICT (D0318-21-1008) and the National Fire Agency (20017102) of the Republic of Korea.
© 2022 IEEE.
All Science Journal Classification (ASJC) codes
- Industrial and Manufacturing Engineering
- Electrical and Electronic Engineering