Fog Radio Access Networks Research Articles

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.

A Realization of Fog-RAN Slicing via Deep Reinforcement Learning

To meet the wide range of 5G use cases in a cost-efficient way, network slicing has been advocated as a key enabler. Unlike the core network slicing in a virtualized environment, radio access network (RAN) slicing is still in its infancy and the corresponding realization is challenging. In this paper, we investigate the realization approach of fog RAN slicing, where two network slice instances for hotspot and vehicle-to-infrastructure scenarios are concerned and orchestrated. In particular, the framework for RAN slicing is formulated as an optimization problem of jointly tackling content caching and mode selection, in which the time-varying channel and unknown content popularity distribution are characterized. Due to the different users’ demands and the limited resources, the complexity of original optimization problem is significant high, which makes traditional optimization approaches hard to be directly applied. To deal with this dilemma, a deep reinforcement learning algorithm is proposed, whose core idea is that the cloud server makes proper decisions on the content caching and mode selection to maximize the reward performance under the dynamical channel state and cache status. The simulation results demonstrate the performance in terms of hit ratio and sum transmit rate can be significantly improved by the proposal.

Resource Allocation for Non-Orthogonal Multiple Access-Enabled Fog Radio Access Networks

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

Multi-Objective Optimization of Task-to-Node Assignment in Opportunistic Fog RAN

The opportunistic fog radio access network (OF-RAN) is a promising RAN architecture proposed for next-generation cellular networks. OF-RAN extends the current fog RAN (F-RAN) architecture by introducing virtual fog access points (v-FAPs) that can be formed on-demand by a set of resourceful end-user devices available in an opportunistic manner. These devices in the v-FAP collaboratively serve as the service nodes to a resource-limited local client by performing resource-demanding processing tasks on its behalf. Hence, appropriately assigning the client task to the service nodes of the v-FAP, i.e., task-to-node assignment (TNA), is a fundamental problem in OF-RAN. This paper formulates and solves the TNA as a multi-objective optimization problem, with the goals of minimizing energy and latency of the v-FAP, while maximizing fairness (or load balancing) amongst its service nodes by minimizing their maximum load.

Decentralized Asynchronous Coded Caching Design and Performance Analysis in Fog Radio Access Networks

In this paper, we investigate the problem of asynchronous coded caching in fog radio access networks (F-RANs). To minimize the fronthaul load, the encoding set collapsing rule and encoding set partition method are proposed to establish the relationship between the coded-multicasting contents for asynchronous and synchronous coded caching. Furthermore, a decentralized asynchronous coded caching scheme is proposed, which provides asynchronous and synchronous transmission methods for different delay requirements. The closed-form expression of the fronthaul load is established for the special case where the number of requests during each time slot is fixed, and the upper and lower bounds of the fronthaul load are given for the general case where the number of requests during each time slot is random. The simulation results show that our proposed scheme can create considerable coded-multicasting opportunities in asynchronous request scenarios.

Machine-Learning Approach for User Association and Content Placement in Fog Radio Access Networks

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

Cellular Fronthaul Offloading Using Device Fogs, Caching, and Network Coding

This paper considers a device-based Fog Radio Access Network (F-RAN), where users’ smart devices (denoted by F-UEs) can be exploited to cache popular files so as to communicate them when requested by other F-UEs among them. This architecture restricts the involvement of the central baseband processing unit (BBU) to serving those F-UEs not immediately served by their peers, thus offloading the BBU's fronthaul spectrum and increasing the overall capacity of the network. This paper aims to further maximize fronthaul offloading in this architecture using network coding (NC). The latter exploits both the cached and previously received files by different F-UEs as side information to serve more requesting F-UEs in each transmission from their peers or the BBU so as to maximize fronthaul offloading. Being half-duplex devices (they can only either send or receive in any given time), the problem of BBU fronthaul offloading in this setting is formulated over two transmission phases on an NC graph. After showing that this problem is NP-hard, the paper proposes two novel heuristic algorithms to solve it in real-time. In addition, a lower-bound on the offloading gain of these algorithms is derived for a special file placement case, their asymptotic optimality is proven, and their complexities are analyzed. Simulation results both show that these proposed algorithms perform closely to the optimal solution and quantify the significant offloading gains achieved by them.

A Mean Field Game-Based Distributed Edge Caching in Fog Radio Access Networks

In this paper, the edge caching optimization problem in fog radio access networks (F-RANs) is investigated. Taking into account time-variant user requests and ultra-dense deployment of fog access points (F-APs), we propose a distributed edge caching scheme to jointly minimize the request service delay and fronthaul traffic load. Considering the interactive relationship among F-APs, we model the optimization problem as a stochastic differential game (SDG) which captures the dynamics of F-AP states. To address both the intractability problem of the SDG and the caching capacity constraint, we propose to solve the optimization problem in a distributive manner. Firstly, a mean field game (MFG) is converted from the original SDG by exploiting the ultra-dense property of F-RANs, and the states of all F-APs are characterized by a mean field distribution. Then, an iterative algorithm is developed that enables each F-AP to obtain the mean field equilibrium and caching control without extra information exchange with other F-APs. Secondly, a fractional knapsack problem is formulated based on the mean field equilibrium, and a greedy algorithm is developed that enables each F-AP to obtain the final caching policy subject to the caching capacity constraint. Simulation results show that the proposed scheme outperforms the baselines.

How Much Can D2D Communication Reduce Content Delivery Latency in Fog Networks With Edge Caching?

A Fog-Radio Access Network (F-RAN) is studied in which cache-enabled Edge Nodes (ENs) with dedicated fronthaul connections to the cloud aim at delivering contents to mobile users. Using an information-theoretic approach, this work tackles the problem of quantifying the potential latency reduction that can be obtained by enabling Device-to-Device (D2D) communication over out-of-band broadcast links. Following prior work, the Normalized Delivery Time (NDT) — a metric that captures the high signal-to-noise ratio worst-case latency — is adopted as the performance criterion of interest. Joint edge caching, downlink transmission, and D2D communication policies based on compress-and-forward are proposed that are shown to be information-theoretically optimal to within a constant multiplicative factor of two for all values of the problem parameters, and to achieve the minimum NDT for a number of special cases. The analysis provides insights on the role of D2D cooperation in improving the delivery latency.

IEEE Access Special Section Editorial: Fog Radio Access Networks (F-RANS) for 5G: Recent Advances and Future Trends

To satisfy the explosively increasing demands for high-speed data applications and massive access requirements of various Internet-of-thing (IoT) devices, a whole package of performance requirements has been proposed for the fifth-generation (5G) mobile communication system. Motivated by the necessity of network architecture enhancement, a paradigm of fog radio access networks (F-RANs) has emerged as a promising evolution path for 5G network architecture. In F-RANs, a fog-computing layer is formed at the edge of networks, and parts of service requirements can be responded to locally without interacting with the cloud computing center via the fronthaul links. Therefore, by taking full advantage of distributed caching and centralized processing, F-RANs provide great flexibility to satisfy quality-of-service (QoS) requirements of various 5G scenarios. F-RAN has become a research hotspot and draws a lot of attention from both academia and industry. As it integrates with artificial intelligence and other new emerging technologies, 5G is facing new challenges, and the study of F-RANs is entering a new era as well.

Dueling Deep-Q-Network Based Delay-Aware Cache Update Policy for Mobile Users in Fog Radio Access Networks

Fog radio access networks (F-RANs) can effectively alleviate fronthaul loads and reduce content transmission delay by migrating cloud services to the network edge. This paper addresses a cooperative caching scenario in F-RAN, where each mobile user can acquire the requested contents from any one of its associated fog-computing-based access points (F-APs). However, caching disparate contents in different F-APs will lead to different content delivery delays, since mobile users suffer from diverse channel fadings and interferences when they download contents from different F-APs. Considering limited caching storage in each F-AP, diverse user preferences, unpredictable user mobility and time-varying channel states, an average transmission delay minimization problem is formulated. With the aid of dueling deep-Q-network framework, a delay-aware cache update policy is proposed for mobile users in F-RAN. The proposed cache update policy will decide to replace the stored contents in F-APs with the proper contents at each time slot. Compared with first in first out, least recently used and least frequently used caching policies, simulation experiments are performed to evaluate the performance of the proposed algorithm. Simulation results illustrate that the proposed caching policy yields better average hit ratio and lower average transmission delay than other traditional caching policies.

On the Opportunities and Challenges of NOMA-Based Fog Radio Access Networks: An Overview

Future generations of wireless networks are expected to provide new services with an unprecedented level of diverse and stringent requirements. Fog Radio Access Network (FRAN) and Non-Orthogonal Multiple Access (NOMA) have emerged as complimentary enablers to meet such requirements. On the one hand, FRAN architecture is designed to reduce the delay caused by the fronthaul link by pushing control and storage to the network edge. On the other hand, in addition to increasing the spectral and energy efficiency and the number of connected devices, NOMA has the potential to improve network latency. This paper overviews the joint benefits of enabling NOMA schemes in an FRAN architecture, by means of examining the applicability and adequateness of the NOMA-based FRAN features in achieving specific objectives of next generation of mobile networks, mainly those related to enhanced mobile broadband (eMBB), massive machine type communications (mMTC), and ultra-reliable low latency communication (URLLC). The paper further depicts the challenges and future research directions that must be addressed in order to meet such opportunities.

AI-Enabled Reliable Channel Modeling Architecture for Fog Computing Vehicular Networks

Artificial intelligence (AI)-driven fog computing (FC) and its emerging role in vehicular networks is playing a remarkable role in revolutionizing daily human lives. Fog radio access networks are accommodating billions of Internet of Things devices for real-time interactive applications at high reliability. One of the critical challenges in today's vehicular networks is the lack of standard wireless channel models with better quality of service (QoS) for passengers while enjoying pleasurable travel (i.e., highly visualized videos, images, news, phone calls to friends/relatives). To remedy these issues, this article contributes significantly in four ways. First, we develop a novel AI-based reliable and interference-free mobility management algorithm (RIMMA) for fog computing intra-vehicular networks, because traffic monitoring and driver's safety management are important and basic foundations. The proposed RIMMA in association with FC significantly improves computation, communication, cooperation, and storage space. Furthermore, its self-adaptive, reliable, intelligent, and mobility-aware nature, and sporadic contents are monitored effectively in highly mobile vehicles. Second, we propose a reliable and delay-tolerant wireless channel model with better QoS for passengers. Third, we propose a novel reliable and efficient multi-layer fog driven inter-vehicular framework. Fourth, we optimize QoS in terms of mobility, reliability, and packet loss ratio. Also, the proposed RIMMA is compared to an existing competitive conventional method (i.e., baseline). Experimental results reveal that the proposed RIMMA outperforms the traditional technique for intercity vehicular networks.

Joint Allocation on Communication and Computing Resources for Fog Radio Access Networks

To improve the user experience, an increasing number of mobile applications offload their computing tasks to servers with powerful computing capabilities. The fog radio access network (F-RAN) incorporates the concept of “fog computing” into the access network architecture, endowing an edge network with computing, storage, communication and control functions. In this paper, we consider a multiple fog access point (F-AP) and a multiuser F-RAN, where each user generates two different tasks: communication and computation. To satisfy the diverse quality of service requirements of different users, we jointly optimize the spectrum access, computation offloading and radio resource allocation. The problem is modeled as a mixed integer nonlinear programming problem, which is difficult to solve. In view of this, we propose a genetic algorithm based on convex optimization, i.e., the genetic convex optimization algorithm (GCOA), which divides the mixed integer nonlinear programming problem into two parts, i.e., optimization and convex optimization, to solve it in polynomial time. Simulation results are provided to verify the effectiveness of the algorithm.

Energy-Aware Cross-Layer Offloading in Fog-RANs Using Network Coded Device Cooperation

This paper studies a Fog Radio Access Network (F-RAN) architecture that utilises the increasing storage and device-to-device communication capacities of users' smart devices (referred to as F-UEs) in order to reduce the time that the central processing unit (referred to as BBU) has to spend to serve these F-UEs and thus, increases the system's capacity. Indeed, these F-UEs can employ these capacities in cooperatively serving each other's file requests, as long as the F-UEs have the files in their storage, rather than always receiving them through the BBU. In addition, network coding (NC) can be employed to minimize the communications among the F-UEs and from the BBU to further offload the BBU resources and reduce the energy consumed in F-UEs' cooperation. This paper develops an algorithm for scheduling the file encoding and transmissions both among half-duplex F-UEs and from the BBU to minimise the consumed time-frequency resources from the BBU given constraints on the energy consumed by each F-UE for their cooperation to help preserve their battery life. The influence of the energy constraint on BBU offloading is investigated and the performance of the system under a variety of settings is evaluated through extensive simulations. The benefit of using NC is shown and the ability of the F-UEs to transmit with different power levels is also studied and shown to considerably impact the number of cooperating F-UEs and the BBU offloading.

Nonsmooth Optimization Algorithms for Multicast Beamforming in Content-Centric Fog Radio Access Networks

This paper considers a content-centric fog radio access network (F-RAN). Its multi-antenna remote radio heads (RRHs) are capable of caching and executing signal processing for content delivery to its users. The fronthaul traffic is thus saved since its baseband processing unit (BBU) needs to transfer only the cache-missed content items to the RRHs via limited-capacity fronthaul links. The problem of beamforming design maximizing the energy efficiency in content delivery subject to the quality-of-content-service constraints in terms of content throughput and fronthaul limited-capacity is addressed. Unlike the user's throughput in user-centric networks, the content throughput in content-centric networks is no longer a differentiable function of the beamforming vectors. The problem is inherently high-dimensional due to the involvement of many beamforming vectors even in simple cases of three RRHs serving three users. Path-following algorithms, which invoke a simple convex quadratic optimization problem to generate a better feasible point, are proposed for computation of this nonsmooth and high-dimensional optimization problem. We also employ generalized zero-forcing beamforming, which forces the multi-content interference to zero or nearly to zero to reduce the problem dimensionality for computational efficiency. Numerical results are provided to demonstrate their computational effectiveness. They also reveal that when the fronthaul traffic becomes more flexible, hard-transfer fronthauling is more energy efficient than soft-transfer fronthauling.

Performance Analysis of Opportunistic Fog Based Radio Access Networks

The advent of the Internet-of-Things (IoT) has led to a rapid growth in data generation. The amount of computation resources required to process the massive data generated by IoT devices, along with the new intelligent use and applications of IoT data such as smart city that can be computation intensive and delay sensitive, have caused an increase in demand for locally available resources at network edge for computation offloading. To address this issue, we have proposed the concept of opportunistic fog radio access network (OF-RAN), which extends the computation capacity of existing fog-RAN (F-RAN) by establishing virtual fog access points (v-FAP) opportunistically using resourceful user devices that participate as service nodes. In this article, we develop an analytical model to evaluate the offloading performance of three RAN architectures: the traditional cloud radio access network (C-RAN), the existing F-RAN, and the proposed OF-RAN. The performances are analyzed in terms of their energy consumption, completion delay, and failure rate, under the effect of varying scenarios.

Content Caching and Delivery in MIMO Fog-RANs With Wireless Fronthaul at Small Cache Size

This letter investigates the caching and delivery strategies in a multiple-input multiple-output (MIMO) fog radio access network (Fog-RAN). We focus on the small cache size scenario where the accumulated cache memories of all edge nodes (ENs) are not enough to cache the whole database. We propose a new file splitting and placement scheme tailored for the small cache size, which offers the freedom to adjust the duplication order of the cached contents to minimize the normalized delivery time (NDT). In the fronthaul delivery, we use multiple antennas to multiplex multiple messages while in the access delivery, transmitter cooperation induced by the duplicated contents among ENs is utilized to cancel interference. By jointly optimizing caching and delivery, we obtain an achievable minimum NDT. It is shown that the achievable NDT is within an additive gap of 1 to the theoretical optimum.

Cooperative caching in fog radio access networks: a graph‐based approach

In this paper, cooperative caching is investigated in fog radio access networks (F-RAN). To maximize the offloaded traffic, cooperative caching optimization problem is formulated. By analyzing the relationship between clustering and cooperation and utilizing the solutions of the knapsack problems, the above challenging optimization problem is transformed into a clustering subproblem and a content placement subproblem. To further reduce complexity, we propose an effective graph-based approach to solve the two subproblems. In the graph-based clustering approach, a node graph and a weighted graph are constructed. By setting the weights of the vertices of the weighted graph to be the incremental offloaded traffics of their corresponding complete subgraphs, the objective cluster sets can be readily obtained by using an effective greedy algorithm to search for the max-weight independent subset. In the graph-based content placement approach, a redundancy graph is constructed by removing the edges in the complete subgraphs of the node graph corresponding to the obtained cluster sets. Furthermore, we enhance the caching decisions to ensure each duplicate file is cached only once. Compared with traditional approximate solutions, our proposed graph-based approach has lower complexity. Simulation results show remarkable improvements in terms of offloaded traffic by using our proposed approach.

Cross-Layer Cloud Offloading With Quality of Service Guarantees in Fog-RANs

Fog radio access networks (F-RANs) have recently been postulated as an innovative solution to improve the fronthaul capacities of cloud base stations (CBSs). This architecture extends the CBS service by involving enhanced remote radio heads (eRRHs), which can pre-store and transmit popular files at the network edge (i.e., close to the end users). This is referred to as caching, and it allows the offloading of CBS resources, e.g., time and frequency. Recent works have been proposed to use rate-aware network coding in order to exploit the previously downloaded popular files at the users’ devices. As such, the CBS offloading is maximized. However, the users’ achieved Quality of Service (QoS), and the standard F-RANs physical-layer resource optimization have not received any attention to date. This paper proposes use of an innovative cross-layer network coding (CLNC) to address the above-mentioned issues. The proposed CLNC scheme is not only aware of different users’ rates but also controls the rates by jointly optimizing coding combinations, users-eRRHs/power zones (PZs) assignments, and transmission power in the PZs. Using a graph theoretical representation, we formulate the joint cross-layer CBS offloading and QoS guarantee problem and show its NP-hardness. Joint and iterative heuristic approaches are then developed to solve this problem using greedy vertex search and coloring techniques. The proposed approaches are finally validated and tested against the existing algorithms in the literature.