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Volume 1 No. 3
05 December 2020
Pricing-based edge caching resource allocation in fog radio access networks
Yanxiang Jiang,Hui Ge,Chaoyi Wan,Baotian Fan,Jie Yan
2020, 1(3): 221-233. doi:
10.23919/ICN.2020.0007
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The edge caching resource allocation problem in Fog Radio Access Networks (F-RANs) is investigated. An incentive mechanism is introduced to motivate Content Providers (CPs) to participate in the resource allocation procedure. We formulate the interaction between the cloud server and the CPs as a Stackelberg game, where the cloud server sets nonuniform prices for the Fog Access Points (F-APs) while the CPs lease the F-APs for caching their most popular contents. Then, by exploiting the multiplier...
Deep reinforcement learning based worker selection for distributed machine learning enhanced edge intelligence in internet of vehicles
Junyu Dong,Wenjun Wu,Yang Gao,Xiaoxi Wang,Pengbo Si
2020, 1(3): 234-242. doi:
10.23919/ICN.2020.0015
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Nowadays, Edge Information System (EIS) has received a lot of attentions. In EIS, Distributed Machine Learning (DML), which requires fewer computing resources, can implement many artificial intelligent applications efficiently. However, due to the dynamical network topology and the fluctuating transmission quality at the edge, work node selection affects the performance of DML a lot. In this paper, we focus on the Internet of Vehicles (IoV), one of the typical scenarios of EIS, and consider the ...
Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks
G. M. Shafiqur Rahman,Tian Dang,Manzoor Ahmed
2020, 1(3): 243-257. doi:
10.23919/ICN.2020.0020
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Fog Radio Access Networks (F-RANs) have been considered a groundbreaking technique to support the services of Internet of Things by leveraging edge caching and edge computing. However, the current contributions in computation offloading and resource allocation are inefficient; moreover, they merely consider the static communication mode, and the increasing demand for low latency services and high throughput poses tremendous challenges in F-RANs. A joint problem of mode selection, resource alloca...
Reinforcement learning based energy-efficient internet-of-things video transmission
Yilin Xiao,Guohang Niu,Liang Xiao,Yuzhen Ding,Sicong Liu,Yexian Fan
2020, 1(3): 258-270. doi:
10.23919/ICN.2020.0021
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The video transmission in the Internet-of-Things (IoT) system must guarantee the video quality and reduce the packet loss rate and the delay with limited resources to satisfy the requirement of multimedia services. In this paper, we propose a reinforcement learning based energy-efficient IoT video transmission scheme that protects against interference, in which the base station controls the transmission action of the IoT device including the encoding rate, the modulation and coding scheme, and t...
Interference management in 6G space and terrestrial integrated networks: Challenges and approaches
Shi Yan,Xueyan Cao,Zile Liu,Xiqing Liu
2020, 1(3): 271-280. doi:
10.23919/ICN.2020.0022
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The Space-Terrestrial Integrated Network (STIN) is considered to be a promising paradigm for realizing worldwide wireless connectivity in sixth-Generation (6G) wireless communication systems. Unfortunately, excessive interference in the STIN degrades the wireless links and leads to poor performance, which is a bottleneck that prevents its commercial deployment. In this article, the crucial features and challenges of STIN-based interference are comprehensively investigated, and some candidate sol...
An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks
Jie Mei,Xianbin Wang,Kan Zheng
2020, 1(3): 281-294. doi:
10.23919/ICN.2020.0019
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Network slicing is a key technology to support the concurrent provisioning of heterogeneous Quality of Service (QoS) in the 5th Generation (5G)-beyond and the 6th Generation (6G) networks. However, effective slicing of Radio Access Network (RAN) is very challenging due to the diverse QoS requirements and dynamic conditions in the 6G networks. In this paper, we propose a self-sustained RAN slicing framework, which integrates the self-management of network resources with multiple granularities, th...
