A deep reinforcement learning-based dynamic traffic offloading in space-air-ground integrated networks (SAGIN)

F Tang, H Hofner, N Kato, K Kaneko… - IEEE Journal on …, 2021 - ieeexplore.ieee.org
Space-Air-Ground Integrated Networks (SAGIN) is considered as the key structure of the
next generation network. The space satellites and air nodes are the potential candidates to …

Optimizing space-air-ground integrated networks by artificial intelligence

N Kato, ZM Fadlullah, F Tang, B Mao… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
It is widely acknowledged that the development of traditional terrestrial communication
technologies cannot provide all users with fair and high quality services due to scarce …

Incorporating distributed DRL into storage resource optimization of space-air-ground integrated wireless communication network

C Wang, L Liu, C Jiang, S Wang… - IEEE Journal of …, 2021 - ieeexplore.ieee.org
Space-air-ground integrated network (SAGIN) is a new type of wireless network mode. The
effective management of SAGIN resources is a prerequisite for high-reliability …

Space-air-ground integrated multi-domain network resource orchestration based on virtual network architecture: A DRL method

P Zhang, C Wang, N Kumar… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Traditional ground wireless communication networks cannot provide high-quality services
for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due …

Latency-aware offloading in integrated satellite terrestrial networks

W Abderrahim, O Amin, MS Alouini… - IEEE Open Journal of …, 2020 - ieeexplore.ieee.org
Next-generation communication networks are expected to integrate newly-used
technologies in a smart way to ensure continuous connectivity in rural areas and to alleviate …

Deep reinforcement learning-based task offloading in satellite-terrestrial edge computing networks

D Zhu, H Liu, T Li, J Sun, J Liang… - 2021 IEEE Wireless …, 2021 - ieeexplore.ieee.org
In remote regions (eg, mountain and desert), cellular networks are usually sparsely
deployed or unavailable. With the appearance of new applications (eg, industrial automation …

A deep reinforcement learning based approach for cost-and energy-aware multi-flow mobile data offloading

C Zhang, Z Liu, B Gu, K Yamori… - IEICE Transactions on …, 2018 - search.ieice.org
With the rapid increase in demand for mobile data, mobile network operators are trying to
expand wireless network capacity by deploying wireless local area network (LAN) hotspots …

A deep-learning-based radio resource assignment technique for 5G ultra dense networks

Y Zhou, ZM Fadlullah, B Mao, N Kato - IEEE Network, 2018 - ieeexplore.ieee.org
Recently, deep learning has emerged as a state-of-the-art machine learning technique with
promising potential to drive significant breakthroughs in a wide range of research areas. The …

Bidirectional mission offloading for agile space-air-ground integrated networks

S Zhou, G Wang, S Zhang, Z Niu… - IEEE Wireless …, 2019 - ieeexplore.ieee.org
SAGIN provides great strength in extending the capability of ground wireless networks. On
the other hand, with rich spectrum and computing resources, ground networks can also …

Content-aware proactive caching for backhaul offloading in cellular network

KN Doan, T Van Nguyen, TQS Quek… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Proactive caching is considered a cost-effective method to address the backhaul bottleneck
problem in cellular network. In this paper, we propose a novel popularity predicting-caching …