Fog Radio Access Networks Research Articles

Optimization of IoT perceived content caching in F-RANs: Minimum retrieval delay and resource extension with performance sensitivity

In the Internet of Things (IoT) perceived applications of monitoring the states of the environment, a feasible technology is to use fog radio access networks (F-RANs) to alleviate the problems of long response time and cloud server bottlenecks in cloud computing. In response to the above problems, this work investigates the problem of minimizing the retrieval delay of IoT contents in F-RANs under the constraints of system resources. The problem is formulated as an integer linear programming (ILP) model. Then, a polynomial-time method with linear programming (LP) relaxation and rounding is proposed to approximate the optimal solution of the problem. Through proof, the method can obtain a feasible solution with a bounded approximation ratio in polynomial time. The conducted simulations validate that the obtained feasible solution is very close to the optimal one. On the other hand, when the system resources are not enough to meet the continuous growth of content retrieval and need to be expanded, this work further establishes an association relation between cached contents and system resources. Based on the above relation, the second method of expanding system resources with performance sensitivity is proposed to provide the service provider with an effective and economical expansion of system resources. It utilizes a predefined system parameter in balancing the trade-off between the approximation ratio to the optimal solution of the problem and the extended system resources. The solution obtained by the second method is also proved to have a bounded approximation ratio.

QoS-driven resource allocation in fog radio access network: A VR service perspective.

While immersive media services represented by virtual reality (VR) are booming, They are facing fundamental challenges, i.e., soaring multimedia applications, large operation costs and scarce spectrum resources. It is difficult to simultaneously address these service challenges in a conventional radio access network (RAN) system. These problems motivated us to explore a quality-of-service (QoS)-driven resource allocation framework from VR service perspective based on the fog radio access network (F-RAN) architecture. We elaborated details of deployment on the caching allocation, dynamic base station (BS) clustering, statistical beamforming and cost strategy under the QoS constraints in the F-RAN architecture. The key solutions aimed to break through the bottleneck of the network design and to deep integrate the network-computing resources from different perspectives of cloud, network, edge, terminal and use of collaboration and integration. Accordingly, we provided a tailored algorithm to solve the corresponding formulation problem. This is the first design of VR services based on caching and statistical beamforming under the F-RAN. A case study provided to demonstrate the advantage of our proposed framework compared with existing schemes. Finally, we concluded the article and discussed possible open research problems.

Access Points Cooperation Based Secretive Coded Caching in Fog Radio Access Networks

Access Points Cooperation Based Secretive Coded Caching in Fog Radio Access Networks

Communication-Efficient Federated Deep Reinforcement Learning based Cooperative Edge Caching in Fog Radio Access Networks

Communication-Efficient Federated Deep Reinforcement Learning based Cooperative Edge Caching in Fog Radio Access Networks

Spatial modeling and reliability analyzing of reconfigurable intelligent surfaces-assisted Fog-RAN with repulsion

Reconfigurable Intelligent Surface (RIS), fog computing, and Cell-Free (CF) network architecture are three promising technologies for application to the Ultra-Reliable Low Latency Communication (URLLC) scenario in 6G mobile communication systems. This paper considers a RIS-assisted FogRadio Access Network (Fog-RAN) architecture where a) the repulsively distributed Fog-Access Points (F-APs) communicate in a CF manner to suppress inter-cell interference, b) RISs are introduced into the CF network to avoid shadowing and enhance the system performance, and c) fog computing evolved as cloud services providers at the edge of the network and an enabler for constructing a multi-layer computing power RAN. Then, we derive and validate the integral form of the maximum F-AP offloading probability and Successful Delivery Probability (SDP) of this RIS-assisted Fog-RAN over composite Fisher-Snedecor F fading, where the spatial effects are reconsidered with the assumption that the F-APs are modelled as a Beta Ginibre Point Process (β-GPP). The numeric and simulation results indicate that for the investigated RIS-assisted Fog-RAN, the β-GPP-based deployment of F-APs can increase maximum of 8% of the SDP within the repulsion-effective range, compared with the Matern Cluster Process (MCP)-based ones. Also, deploying more RISs per F-AP offers more significant SDP improvements.

Modeling and analysis of computation offloading in NOMA-based fog radio access networks

Modeling and analysis of computation offloading in NOMA-based fog radio access networks

Spectral Efficiency Improvement in Downlink Fog Radio Access Network With Deep-Reinforcement-Learning-Enabled Power Control

Fog radio access network (F-RAN) is a promising architecture that leverages edge computing and caching to improve devices’ latency and quality of service. However, interference, which arises when multiple devices are concurrently scheduled on the same radio resource block (RRB), limits the performance of a dense F-RAN. This paper considers a multi-cell F-RAN in which the devices of each small cell receive data from the associated fog access point (F-AP) over the same RRB(s). The F-APs transmit data to the associated devices using rate-splitting multiple access (RSMA) schemes to manage co-channel interference within the small cells efficiently. A transmit power control scheme is proposed to maximize the network’s spectral efficiency (SE) while considering the devices’ hardware impairments (HWIs). The considered transmit power control scheme is an NP-hard problem, which is highly challenging to solve using the legacy optimization approach. To address this challenge, we propose a distributed deep reinforcement learning (DRL)-based power allocation (DDPA) scheme that takes the time-varying dynamics of the network and the HWIs of devices into account. Each F-AP in the proposed framework is equipped with a DRL agent that collects signal-to-interference-plus-noise ratio and channel state information from connected devices and adapts the transmit power allocation each scheduling interval. In addition, the ensemble learning framework is exploited to further improve the proposed DDPA scheme’s performance. We use extensive simulations to demonstrate that the DDPA scheme achieves greater SE than contemporary transmit power control schemes. In particular, the proposed DDPA scheme is especially suited to scenarios with non-negligible HWIs-induced distortion.

Efficient Network Slicing for 5G Services in Cloud Fog-RAN Deployment Over WDM Network

Network slicing in fifth generation (5G) radio access network (RAN) enables serving massive network traffic with diverse and stringent quality of service (QoS) requirements. Multiple logical networks can be built using RAN slicing over a single RAN infrastructure. This paper considers three different types of slices that are standardized by third generation partner project (3GPP): ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and massive machine type communication (mMTC). Each slice type has unique requirements for data rate, latency, and reliability. Cloud RAN (C-RAN) was proposed to address the requirements of 5G services, which involves physical separation between the remote radio head (RRH) and baseband unit (BBU) in 2-layers. While C-RAN enables more efficient resource utilization and energy consumption, it limits network scalability. Furthermore, C-RAN is unable to meet the stringent latency demands of URLLC services, as well as the massive fronthaul capacity required for eMBB requests. To address this issue, we propose a novel cloud fog RAN (CF-RAN) over wavelength division multiplexing (WDM) architecture. In CF-RAN over WDM the RAN functions are divided into 3 layers, which include the RRH at layer 1, fog nodes at layer 2, and BBU hotels at layer 3. We employ the emerging fog computing paradigm in optical and wireless networks in this suggested architecture. To facilitate low latency URLLC requests, the fog nodes are located closer to the cell site (CS). We propose an integer linear programming (ILP)-based mathematical model. The model's objective is to decrease the number of active BBU hotels and fog nodes while remaining compliant with practical network restrictions. Furthermore, a low complexity greedy heuristic algorithm is proposed to solve the problem and is compared to the branch & bound (B&B) algorithm, which is assumed to provide an optimal solution with exponential complexity. The proposed 3-layer CF-RAN over WDM architecture achieves a 70% improvement in BBU centralization and a 50% reduction in request blocking when compared to the standard 2-layer architecture.

Joint user association and resource allocation for cost-efficient NOMA-enabled F-RANs

Integrating Non-Orthogonal Multiple Access (NOMA) into Fog Radio Access Networks (F-RANs) has shown to be effective in boosting the spectral efficiency, energy efficiency, connectivity, and reducing the latency, thus attracting significant research attention. However, the performance improvement of the NOMA-enabled F-RANs is at the cost of computational overheads, which are commonly neglected in their design and deployment. To address this issue, in this paper, we propose a hybrid dynamic downlink framework for NOMA-enabled F-RANs. In this framework, we first develop a novel network utility function, which takes both the network throughput and computational overheads into consideration, thus enabling us to comprehensively evaluate the performance of different access schemes for F-RANs. Based on the developed network utility function, we further formulate a network utility maximization problem, subject to practical constraints on the decoding order, power allocation, and quality-of-service. To solve this NP-hard problem, we decompose it into two subproblems, namely, a user equipment association and subchannel assignment subproblem and a power allocation subproblem. Three-dimensional matching and sequential convex programming-based algorithms are designed to solve these two subproblems, respectively. Through numerical results, we show how our proposed algorithms can achieve a good balance between the network throughput and computational overheads by judiciously adjusting the maximum transmit power of fog access points. We also show that the proposed NOMA-enabled F-RAN framework can increase, by up to 89%, the network utility, compared to OMA-based F-RANs.

Secretive Coded Caching With Nonuniform Demands in Fog Radio Access Networks

In this letter, we investigate the problem of secretive coded caching in fog radio access networks (F-RANs), aiming at minimizing the average backhaul rate under nonuniform file popularity distribution. Firstly, a two-layer placement scheme which jointly considers secrecy constraints and cache resources in the network is designed. Next, a two-stage delivery scheme which utilizes the network architecture characteristics and exploits the redundancy in user demands is designed. By integrating the above two schemes, a novel secretive coded caching scheme is proposed. Finally, we derive the expression of the average backhaul rate and evaluate the performance of the proposed scheme via simulations. Simulation results show that our proposed scheme outperforms the state of the art in terms of backhaul rate reduction.

Latency minimization for multiuser computation offloading in fog-radio access networks

Recently, the Fog-Radio Access Network (F-RAN) has gained considerable attention, because of its flexible architecture that allows rapid response to user requirements. In this paper, computational offloading in F-RAN is considered, where multiple User Equipments (UEs) offload their computational tasks to the F-RAN through fog nodes. Each UE can select one of the fog nodes to offload its task, and each fog node may serve multiple UEs. The tasks is computed by the fog nodes or further offloaded to the cloud via a capacity-limited fronhaul link. In order to compute all UEs' tasks quickly, joint optimization of UE-Fog association, radio and computation resources of F-RAN is proposed to minimize the maximum latency of all UEs. This min-max problem is formulated as a Mixed Integer Nonlinear Program (MINP). To tackle it, first, MINP is reformulated as a continuous optimization problem, and then the Majorization Minimization (MM) method is used to find a solution. The MM approach that we develop is unconventional in that each MM subproblem is solved inexactly with the same provable convergence guarantee as the exact MM, thereby reducing the complexity of MM iteration. In addition, a cooperative offloading model is considered, where the fog nodes compress-and-forward their received signals to the cloud. Under this model, a similar min-max latency optimization problem is formulated and tackled by the inexact MM. Simulation results show that the proposed algorithms outperform some offloading strategies, and that the cooperative offloading can exploit transmission diversity better than noncooperative offloading to achieve better latency performance.

On the Performance of Clustered Fog Radio Access Networks With Data Compression

The fog-radio-access-network (F-RAN) has been proposed to address the strict latency requirements, which offloads computation tasks generated in the user equipment (UE) to the edge to reduce the processing latency. However, it incorporates the task transmission latency, which may become the bottleneck of latency requirements. Data compression (DC) has been considered as one of the promising techniques to reduce the transmission latency. By compressing the computation tasks before transmitting, the transmission delay is reduced due to the shrink transmitted data size, and the original computing task can be retrieved by employing data decompressing (DD) at the edge nodes or the centre cloud. Nevertheless, the DC and DD incorporate extra processing latency. For the F-RAN system, the latency performance has not been investigated considering the DC and DD processes. Therefore, in this work, the successful data compression probability (SDCP), i.e., the probability of the task execution latency being smaller than a target latency and the signal to interference ratio (SIR) of the received signal being higher than a threshold, is defined to analyse the latency performance of the DC-enabled F-RAN. Moreover, to analyse the impact of compression offloading ratio (COR), which determines the proportion of tasks being compressed at the edge, on the SDCP of the F-RAN, a novel hybrid compression mode is proposed based on the queueing theory. Based on this, the closed-form result of SDCP in the large-scale DC-enabled F-RAN is derived by combining the Matern cluster process and M/G/1 queueing model, and validated by the Monte-Carlo simulation. Based on the derived SDCP results, the effects of COR on the SDCP is analysed numerically. The results show that the SDCP with the optimal COR can be enhanced with a maximum value of 0.3 and 0.55 as compared with the cases of compressing all computing tasks at the edge and at the UE, respectively. Moreover, for the system requiring the minimal latency, the proposed hybrid compression mode can alleviate the requirement on the backhaul capacity.

Resource-Aware Video Delivery in Fog Radio Access Networks: A Joint QoE and QoS Perspective

In this paper, the video delivery strategy is studied under the cooperative framework of cache-enabled fog radio access networks (F-RAN), where multicast beamforming and video-quality selection are both taken into account in terms of different scenarios. In the user-centered scenario, an optimization problem regarding to the network quality of experience (QoE) improvement is first formulated under the essential quality of service (QoS) requirements of the users. Furthermore, an energy efficiency maximization scheme is proposed in the user-dense scenario to improve the resource utilization when network resources may be insufficient to guarantee the QoS requirements of all the users. In particular, a user scheduling scheme is studied in this case. More specifically, the non-convex constraints and fractional object functions in the aforementioned problems are dealt with by successive convex approximation (SCA) technique and the Dinkelbach approach. In order to solve the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{0}$</tex-math></inline-formula> -norm problem caused by user scheduling, we introduce the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm approximation to improve the sparsity of the solution. Finally, the effectiveness of the proposed schemes is validated by extensive simulations.

Joint Optimization of Resources in Fog-Radio Access Network with Binary Computation Offloading

With the dramatic increase in the number of emerging Internet services, the Fog-Radio Access Network (F-RAN) has recently emerged as a promising paradigm to enhance high-load task processing capabilities for mobile devices, such as the Internet of things (IoT) and mobile terminals. Hence, it becomes a challenge for the F-RAN to reduce the offloading cost by designing an effective offloading strategy and rational planning of limited network resources to improve the quality of experience (QoE). This article investigates the F-RAN with a binary offload policy. It proposes an intelligent algorithm capable of optimally adapting to task offload policy, fog computing resource allocation, and offload channel resource allocation. To evaluate the offloading strategy intuitively, we design a system utility metric defined as a delay-energy weighted sum. The joint optimization problem is converted into a convex problem based on this metric, i.e., a mixed integer nonlinear programming (MINLP) problem. A novel algorithm based on improved double-deep Q neural networks is DDQN, which is proposed to address this problem. Furthermore, an action space mapping method in the DDQN framework is presented to obtain offloading decisions. Extensive experimental data indicate that the proposed DDQN algorithm can effectively reduce the offloading cost and is adaptable to different offloading scenarios.

Energy Efficiency and Delay Optimization of Virtual Slicing of Fog Radio Access Network

To develop the energy efficient of 6G networks, the fog radio access network (F-RAN) is expected to meet various use cases of high-performance mobile services. Although network virtualization greatly enhances the flexibility to accommodate various services, the interaction and optimization between delay and energy efficiency (EE) in virtual slicing (VS)-based F-RAN have not been well studied. To accomplish the EE and delay optimization of VS of F-RAN. The key technical challenges lie in the construction of new network architecture, the integration and optimization of radio, caching, and computing 3-D resources, reducing the algorithm’s complexity, and simulation verification. We first design a novel network architecture based on VS and fog computing. The VS method in F-RAN to embed VS assembles virtual radio, caching, and computing resources into physical substrates, transforming nonconvex problems into convex optimization problems by transforming constraints and using Lyapunov optimization methods. We further propose a virtual resource allocation optimization algorithm based on EE. To reduce the complexity of the algorithm, a low-complexity EE optimization algorithm is further proposed for virtual resource allocation. The simulation results show that the low-complexity virtual resource allocation EE optimization algorithm proposed in this article has better performance than the existing fog access network resource allocation methods. The EE is further improved by about 30% under the condition that the guaranteed delay threshold is two slots (i.e., 1 ms).

A Joint Learning and Game-Theoretic Approach to Multi-Dimensional Resource Management in Fog Radio Access Networks

Fog radio access networks (F-RANs) have been regarded as a promising paradigm to support latency-sensitive and computation-intensive services by leveraging computing and caching capabilities of fog access points (F-APs). To execute offloaded computation tasks, it is essential to pre-store necessary programs and databases at F-APs, referred as service caching. However, due to system dynamics and the coupling of decisions, cache, radio, and computation resource management at F-RANs is a challenging problem. In this paper, a joint multi-dimensional resource optimization problem is formulated, aiming at minimizing the weighted sum of expected latency cost and caching cost, which is featured by a two-timescale structure. For radio and computation resource allocation on a small timescale, a coalitional game based approach is proposed under given caching decisions. On a larger timescale, services are cached at each F-AP using a multi-agent reinforcement learning algorithm. The advantage of the proposed algorithms is that they can implicitly take the impact of small timescale resource allocation into account while adapting to the long-term statistics of channel coefficients and user service requests. We analyze the proposed algorithms with respect to their convergence, optimality, and complexity, and validate their performance with extensive simulations, where superior performance is observed over several baseline schemes.

Content Popularity Prediction Based on Quantized Federated Bayesian Learning in Fog Radio Access Networks

In this paper, we investigate the content popularity prediction problem in cache-enabled fog radio access networks (F-RANs). In order to predict the content popularity with high accuracy and low complexity, we propose a Gaussian process based regressor to model the content request pattern. Firstly, the relationship between content features and popularity is captured by our proposed model. Then, we utilize Bayesian learning to train the model parameters, which is robust to overfitting. However, Bayesian methods are usually unable to find a closed-form expression of the posterior distribution. To tackle this issue, we apply a stochastic variance reduced gradient Hamiltonian Monte Carlo (SVRG-HMC) method to approximate the posterior distribution. To utilize the computing resource of fog access points (F-APs) and also reduce the communication overhead, we propose a quantized federated learning (FL) framework combining with Bayesian learning. The proposed quantized federated Bayesian learning framework allows each F-AP to send gradients to the cloud server after quantizing and encoding. It can achieve a tradeoff between prediction accuracy and communication overhead effectively. Simulation results show that the performance of our proposed policy outperforms the considered baseline policies.

Multi-Domain Resource Scheduling for Simultaneous Wireless Computing and Power Transfer in Fog Radio Access Network

Future 6G is deemed to provide Simultaneous Wireless cOmputing and Power Transfer (SWOPT) services for addressing the shortage of local computation and that of energy at terminal devices in the era of the Internet of Everything (IoE). In this paper, we study a fog radio access network (F-RAN) consisting of enhanced remote radio heads (eRRHs) with multiple antennas and edge computing capabilities. The F-RAN simultaneously supports task offloading of computing users (CUs) and energy harvesting of energy users (EUs). Thanks to the global coordination of a central server, full cooperation among eRRHs in both computing and communication domains can substantially improve the SWOPT performance. Specifically, the energy harvesting performance of EUs is maximised by jointly optimising the bandwidth and the beam resources in the communication domain as well as the computing frequency and the offloading strategy in the computing domain. The original non-convex problem with mixed integer and continuous variables is then solved by sequential convex optimisations in the framework of a greedy algorithm with low complexity. Thorough simulation results demonstrate the advantage of our proposed resource scheduling scheme over the existing state-of-the-art counterparts.

Partially Collaborative Edge Caching Based on Federated Deep Reinforcement Learning

In this paper, edge caching is investigated in fog radio access networks subject to dynamic content popularity. In order to improve the long-term cache-hit-ratio (CHR), a federated deep reinforcement learning (FDRL) framework is proposed, in which multiple fog access points (FAPs) adjust their caching strategies under the coordination of a central server (CS). Owing to the non-i.i.d data acquired over different FAPs, the traditional federated learning (FL) method may suffer from performance loss due to maintaining a global model at the CS. To address this shortcoming, a partially collaborative caching (PCC) algorithm is proposed by switching the data training between two models that are maintained at each FAP, to achieve a balance between the users' specific local characteristics and holistic global characteristics. Experiments on a real-world dataset demonstrate that significant performance gains are achieved by the proposed FDRL in terms of CHR. Furthermore, with an appropriate switching factor, the proposed PCC algorithm outperforms FL with full collaboration among FAPs.

Deep reinforcement learning‐based joint optimization of computation offloading and resource allocation in F‐RAN

AbstractThe fog radio access network (F‐RAN) has been regarded as a promising wireless access network architecture in the fifth generation (5G) and beyond systems to satisfy the increasing requirements for low‐latency and high‐throughput services by providing fog computing. However, because the cloud computing centre and fog computing‐enabled access points (F‐APs) in the F‐RAN have different computation and communication capabilities, it is crucial to make an efficient computation offloading and resource allocation strategy that can fully exploit the potential of the F‐RAN system. In this paper, the authors investigate a decentralized low‐complexity deep reinforcement learning (DRL)‐based framework for joint computation task offloading and resource allocation in the F‐RAN, which supports assistive computing‐enabled tasks offloading between F‐APs. Considering the constraints of task latency, wireless transmission rate, transmission power, and computational resource capacity, the authors formulate the system processing efficiency maximization problem by jointly optimizing offloading mode selection, channel allocation, power control, and computation resource allocation in the F‐RAN. To solve this non‐linear and non‐convex problem, the authors propose a federated DRL‐based computation offloading and resource allocation algorithm to improve the task processing efficiency and ensure privacy in the system, which can significantly reduce the computing complexity and signalling overhead of the training process compared with the centralized learning‐based method. Specifically, each local F‐AP agent consists of dueling deep Q‐network (DDQN) and deep deterministic policy gradient (DDPG) networks to appropriately deal with discrete and continuous valuable action spaces, respectively. Finally, the simulation results show that the proposed federated DRL algorithm can achieve significant performance improvements in terms of system processing efficiency and task latency compared with other benchmarks.