Fog Computing Based Radio Access Networks Research Articles

Joint Radio Resource Allocation and Beamforming Optimization for Industrial Internet of Things in Software-Defined Networking-Based Virtual Fog-Radio Access Network 5G-and-Beyond Wireless Environments

Fog computing-based radio access network (Fog-RAN) leveraging the software-defined networking (SDN) and network function virtualization (NFV) is the most promising solution to offer real-time support for the massive number of connected devices in the industrial Internet of Things (IIoT) networks. However, designing an optimal dynamic radio resource allocation to handle the fluctuating traffic loads is critical. In this article, a novel architectural design of an SDN-based virtual Fog-RAN is proposed, in which we jointly study radio resource allocation and transmit beamforming to improve resource utilization and IIoT users’ satisfaction, by minimizing the network power consumption (NPC) and maximizing the achievable sum-rate (ASR), simultaneously. To this end, we first formulate a mixed-integer nonlinear problem to optimize the physical resource block allocation, the assignment of user equipments, and radio unit, and the downlink transmit beamforming, by considering imperfect channel state information. To solve the ntractable MINLP, we exploit the successive convex approximation approach. Then, we formulate a multiple knapsack problem (MKP) to optimize the assignment between RUs and virtual baseband units, by exploiting the set of active RUs minimized in the previous problem. We solve the formulated MKP by decomposing the dual problems and solving them through the dual descent method. Through performance analysis, we show the proposed approach provides a high users’ satisfaction rate, maximizes the ASR and minimizes the NPC, and provides better savings, in terms of the number of radio and baseband resources utilized, than its counterparts.

Artificial Intelligence Aided Joint Bit Rate Selection and Radio Resource Allocation for Adaptive Video Streaming over F-RANs

Recently, fog-computing-based radio access networks (F-RANs) have been conceptualized to provide high quality of experience (QoE) for adaptive bit rate (ABR) streaming, where additional computing capacity is supplemented on fog nodes to facilitate complicated cross-layer optimization (i.e., joint bit rate selection and radio resource allocation). However, finding an optimal global solution with acceptable complexity is still infeasible by the conventional optimization methods. In this work, we propose an artificial intelligence (AI) aided joint bit rate selection and radio resource allocation scheme referred to as iABR, which provides a new vision for handling the over-complicated optimization in F-RANs. Based on multi-agent hierarchy deep reinforcement learning, the proposed iABR can dynamically allocate radio resource and select bit rate in a multiuser scenario, by perceiving the network environment and clients' player information. Moreover, long short-term memory (LSTM) is employed by the iABR algorithm, which enables accurate prediction of the change of channel quality by learning the history of the wireless channel. Hence, iABR is able to adjust the action policy in advance to accommodate the future channel quality for avoiding bit rate fluctuation. According to the experimental results, the iABR exhibits higher QoE in terms of high average bit rate, low rebuffering ratio, and average bit rate variance. Last but not least, the QoE performance of all the clients are fairly guaranteed by the iABR algorithm, enhancing the practicality of AI-driven F-RANs for multimedia service delivery.

Artificial-Intelligence-Driven Fog Radio Access Networks: Recent Advances and Future Trends

The articles in this special section focus on artificial intelligence-driven fog radio access networks. To satisfy the explosively increasing demands of highspeed data applications and access requirements from a massive number of Internet-of-Things (IoT) devices, a paradigm of fog computing-based radio access network (F-RAN) has emerged as a promising evolution path for the fifth generation (5G) radio access networks. By taking full advantage of distributed caching and centralized processing, F-RANs provide great flexibility to satisfy the quality-of-service requirements of various 5G services. With the rapid deployment of 5G communication networks, the application of F-RANs to the envisioned sixth-generation (6G) mobile network has attracted extensive attention from academia, industry, and government agencies. The 6G network progress in enhanced mobile broadband, massive machine-type communications and ultra-reliable and low-latency communications will lead to the fast development of new applications, including augmented reality (AR), virtual reality (VR), holographic communications, vehicle-to-everything (V2X), self-driving cars, massive sensors connective on the ground and several tens of thousands of satellites connective in the sky.

Price-based Video Caching via Stackelberg Game in F-RAN

In this paper, the video caching problem in fog computing based radio access networks (F-RAN) to reduce video transmission latency is investigated. According to the geographical factors, fog access points (F-APs) are divided into multiple F-AP clusters. To find the optimal video caching strategy, we propose a Stackelberg game by regarding videos as a specific type of resources. In the proposed game, the cloud server acting as the leader leases its videos, while F-AP clusters acting as followers can efficiently reduce video transmission latency after placing these videos to their F-APs. We establish the profit functions for both the cloud server and F-AP clusters. Then, the Stackleberg equilibrium is investigated by solving the optimization problems. Numerical results are provided for verifying the effectiveness of the proposed model by showing the video pricing and placement strategies.

Fog Computing as an Enabler for Immersive Media: Service Scenarios and Research Opportunities

Immersive media services, such as augmented reality and virtual reality (AR/VR), a 360-degree video, and free-viewpoint video (FVV), are popular today. They require massive data storage, ultrahigh computing power, and ultralow latency. It is hard to fulfill these requirements simultaneously in a conventional communication system using a cloud/centralized radio access network (C-RAN). Specifically, due to centralized processing in such a system, the end-to-end latency, as well as the burden on the fronthaul network, are expected to be high. Fog computing-based radio access networks (F-RAN), in contrast, have been widely considered as an enabler for immersive media. Our contribution in this paper is threefold: First, we propose various service scenarios reflecting the characteristics of immersive media. Second, we identify the technologies that are required to support the proposed service scenarios under F-RAN and discuss how they can support the proposed scenarios efficiently. Third, we discuss possible research opportunities. a A list of acronyms can be found in the Appendix.

Efficiently Secure Broadcasting in 5G Wireless Fog-Based-Fronthaul Networks

Enhanced Diversity and Network Coding (eDC-NC), the synergistic combination of Diversity and modified Triangular Network Coding, was introduced recently to provide efficient and ultra-reliable networking with near-instantaneous fault recovery. In this paper it is shown that eDC-NC technology can efficiently and securely broadcast messages in 5G wireless fog-computing-based Radio Access Networks (F-RAN). In particular, this work is directed towards demonstrating the ability of eDC-NC technology to more efficiently provide secure messages broadcasting than standardized methods such as Secure Multicasting using Secret (Shared) Key Cryptography, such that the adversary has no ability to acquire information even if they wiretap the entire F-RAN network (except of course the source and destination nodes). Our results show that using secure eDC-NC technology in F-RAN fronthaul network will enhance secure broadcasting and provide ultra-reliability networking, near-instantaneous fault recovery, and retain the throughput benefits of Network Coding.

Edge computing technologies for Internet of Things: a primer

With the rapid development of mobile internet and Internet of Things applications, the conventional centralized cloud computing is encountering severe challenges, such as high latency, low Spectral Efficiency (SE), and non-adaptive machine type of communication. Motivated to solve these challenges, a new technology is driving a trend that shifts the function of centralized cloud computing to edge devices of networks. Several edge computing technologies originating from different backgrounds to decrease latency, improve SE, and support the massive machine type of communication have been emerging. This paper comprehensively presents a tutorial on three typical edge computing technologies, namely mobile edge computing, cloudlets, and fog computing. In particular, the standardization efforts, principles, architectures, and applications of these three technologies are summarized and compared. From the viewpoint of radio access network, the differences between mobile edge computing and fog computing are highlighted, and the characteristics of fog computing-based radio access network are discussed. Finally, open issues and future research directions are identified as well.

Cloud computing based radio access network

Cloud computing based radio access network (CCRAN) is a promising new paradigm for achieving high spectral efficiency (SE) and energy efficiency (EE) performances through embedding cloud computing into the wireless communication system. Cloud radio access network (C-RAN), heterogeneous cloud radio access network (H-CRAN), and fog computing based radio access network (F-RAN) as three application examples of CC-RANs that have gained significant traction in the research community and industry recently.

Fog-computing-based radio access networks: issues and challenges

A fog computing based radio access network (F-RAN) is presented in this article as a promising paradigm for the fifth generation (5G) wireless communication system to provide high spectral and energy efficiency. The core idea is to take full advantages of local radio signal processing, cooperative radio resource management, and distributed storing capabilities in edge devices, which can decrease the heavy burden on fronthaul and avoid large-scale radio signal processing in the centralized baseband unit pool. This article comprehensively presents the system architecture and key techniques of F-RANs. In particular, key techniques and their corresponding solutions, including transmission mode selection and interference suppression, are discussed. Open issues in terms of edge caching, software-defined networking, and network function virtualization, are also identified.