Wang, Y.; Li, B.; He, J.; Dai, J.; Liu, Y.; Yang, Y. Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology. Electronics2023, 12, 3537.
Wang, Y.; Li, B.; He, J.; Dai, J.; Liu, Y.; Yang, Y. Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology. Electronics 2023, 12, 3537.
Wang, Y.; Li, B.; He, J.; Dai, J.; Liu, Y.; Yang, Y. Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology. Electronics2023, 12, 3537.
Wang, Y.; Li, B.; He, J.; Dai, J.; Liu, Y.; Yang, Y. Joint Latency-Oriented, Energy Consumption, and Carbon Emission for a Space–Air–Ground Integrated Network with Newly Designed Power Technology. Electronics 2023, 12, 3537.
Abstract
The ubiquitous connectivity for the space-air-ground integrated network (SAGIN) of the beyond fifth generation of communication and sixth generation of communication (B5G/6G) is envisaged to meet the needs for the demanded quality of service (QoS), green communication, and "dual carbon" target. However, the offloading and computation of massive latency-sensitive tasks dramatically increases the energy consumption of the network. Furthermore, the traditional power supply technology of the network base stations (BSs) enhances the carbon emission. To address these issues, we first propose a SAGIN architecture with energy harvesting devices, where the BS is powered by both renewable energy (RE) and the conventional grid. The BS explores wireless power transfer (WPT) technology to power the unmanned aerial vehicle (UAV) for stable network operation. RE sharing between neighbouring BSs is designed to fully utilize RE for reduce carbon emission. Secondly, on the basis of task offloading decision, UAV trajectory, and RE sharing ratio, we construct cost functions with joint latency-oriented, energy consumption, and carbon emission. Then, we develop a twin delayed deep deterministic policy gradient (TD3PG) algorithm based on deep reinforcement learning to minimize the cost function. Finally, simulation results demonstrate that the proposed algorithm outperforms the benchmark algorithm in terms of reducing latency, energy saving, and lower carbon emission.
Keywords
space-air-ground integrated network; renewable energy; twin delayed deep deterministic policy gradient; latency; energy consumption
Subject
Computer Science and Mathematics, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
