Joint Resource Allocation and Admission Control in Sliced Fog Radio Access Networks

Network slicing based fog radio access network(F-RAN) has emerged as a promising architecture to support various novel applications in 5 G-and-beyond wireless networks. However, the co-existence of multiple network slices in F-RANs may lead to significant performance degradation due to the resource competitions among different network slices. In this paper, the downlink F-RANs with a hotspot slice and an Internet of Things(Io T) slice are considered, in which the user equipments(UEs) of different slices share the same spectrum. A novel joint resource allocation and admission control scheme is developed to maximize the number of UEs in the hotspot slice that can be supported with desired quality-of-service, while satisfying the interference constraint of the UEs in the Io T slice. Specifically, the admission control and beamforming vector optimization are performed in the hotspot slice to maximize the number of admitted UEs, while the joint sub-channel and power allocation is performed in the Io T slice to maximize the capability of the UEs in the Io T slice tolerating the interference from the hotspot slice. Numerical results show that our proposed scheme can effectively boost the number of UEs in the hotspot slice compared to the existing baselines.

Hierarchical Content Caching in Fog Radio Access Networks:Ergodic Rate and Transmit Latency

In order to alleviate capacity constraints on the fronthaul and decrease the transmit latency, a hierarchical content caching paradigm is applied in the fog radio access networks(F-RANs). In particular, a specific cluster of remote radio heads is formed through a common centralized cloud at the baseband unit pool, while the local content is directly delivered at fog access points with edge cache and distributed radio signal processing capability. Focusing on a downlink F-RAN, the explicit expressions of ergodic rate for the hierarchical paradigm is derived. Meanwhile, both the waiting delay and latency ratio for users requiring a single content are exploited. According to the evaluation results of ergodic rate on waiting delay, the transmit latency can be effectively reduced through improving the capacity of both fronthaul and radio access links. Moreover, to fully explore the potential of hierarchical content caching, the transmit latency for users requiring multiple content objects is optimized as well in three content transmission cases with different radio access links. The simulation results verify the accuracy of the analysis, further show the latency decreases significantly due to the hierarchical paradigm.

The Interplay between Artificial Intelligence and Fog Radio Access Networks

The interplay between artificial intelligence(AI) and fog radio access networks(F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning(RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatio-temporal unknown content popularity to showcase the potential of applying AI to F-RANs.

Deep reinforcement learning based computation offloading and resource allocation for low-latency fog radio access networks

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 allocation,and power allocation is formulated to minimize latency under various constraints.We propose a Deep Reinforcement Learning(DRL)based joint computation offloading and resource allocation scheme that achieves a suboptimal solution in F-RANs.The core idea of the proposal is that the DRL controller intelligently decides whether to process the generated computation task locally at the device level or offload the task to a fog access point or cloud server and allocates an optimal amount of computation and power resources on the basis of the serving tier.Simulation results show that the proposed approach significantly minimizes latency and increases throughput in the system.

Pricing-based edge caching resource allocaƟon in fog radio access networks

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 penalty function method,we transform the constrained optimization problem of the cloud server into an equivalent non-constrained one,which is further solved by using the simplex search method.Moreover,the existence and uniqueness of the Nash Equilibrium(NE)of the Stackelberg game are analyzed theoretically.Furthermore,we propose a uniform pricing based resource allocation strategy by eliminating the competition among the CPs,and we also theoretically analyze the factors that affect the uniform pricing strategy of the cloud server.We also propose a global optimization-based resource allocation strategy by further eliminating the competition between the cloud server and the CPs.Simulation results are provided for quantifying the proposed strategies by showing their efficiency in pricing and resource allocation.

Beamforming and fronthaul compression design for intelligent reflecting surface aided cloud radio access networks

Owing to the inherent central information processing and resource management ability,the cloud radio access network(C-RAN)is a promising network structure for an intelligent and simplified sixth-generation(6G)wireless network.Nevertheless,to further enhance the capacity and coverage,more radio remote heads(RRHs)as well as high-fidelity and low-latency fronthaul links are required,which may lead to high implementation cost.To address this issue,we propose to exploit the intelligent reflecting surface(IRS)as an alternative way to enhance the C-RAN,which is a low-cost and energy-efficient option.Specifically,we consider the uplink transmission where multi-antenna users communicate with the baseband unit(BBU)pool through multi-antenna RRHs and multiple IRSs are deployed between the users and RRHs.RRHs can conduct either point-to-point(P2P)compression or Wyner-Ziv coding to compress the received signals,which are then forwarded to the BBU pool through fronthaul links.We investigate the joint design and optimization of user transmit beamformers,IRS passive beamformers,and fronthaul compression noise covariance matrices to maximize the uplink sum rate subject to fronthaul capacity constraints under P2P compression and Wyner-Ziv coding.By exploiting the Arimoto-Blahut algorithm and semi-definite relaxation(SDR),we propose a successive convex approximation approach to solve non-convex problems,and two iterative algorithms corresponding to P2P compression and Wyner-Ziv coding are provided.Numerical results verify the performance gain brought about by deploying IRS in C-RAN and the superiority of the proposed joint design.

Joint uplink and downlink resource allocation for low-latency mobile virtual reality delivery in fog radio access networks

Fog radio access networks(F-RANs),in which the fog access points are equipped with communication,caching,and computing functionalities,have been anticipated as a promising architecture for enabling virtual reality(VR)applications in wireless networks.Although extensive research efforts have been devoted to designing efficient resource allocation strategies for realizing successful mobile VR delivery in downlink,the equally important resource allocation problem of mobile VR delivery in uplink has so far drawn little attention.In this work,we investigate a mobile VR F-RAN delivery framework,where both the uplink and downlink transmissions are considered.We first characterize the round-trip latency of the system,which reveals its dependence on the communication,caching,and computation resource allocations.Based on this information,we propose a simple yet efficient algorithm to minimize the round-trip latency,while satisfying the practical constraints on caching,computation capability,and transmission capacity in the uplink and downlink.Numerical results show that our proposed algorithm can effectively reduce the round-trip latency compared with various baselines,and the impacts of communication,caching,and computing resources on latency performance are illustrated.

Challenges for beyond 5G:ultra-densification of radio access network

Ultra-densification of radio access network(RAN)is a key to efficiently support the exponentially growing mobile data traffic in 5 G era.Furthermore,extremely high frequency band like mm Wave band was utilized to solve the bandwidth shortage problem.However,untra-dense reusing the same radio resource produced severe interference.And the mm Wave link was very harsh due to frequent blockage by obstacles.Therefore a new RAN architecture needed to be introduced to realize ultra-reliable communications in such a severe radio propagation environment.An architecture of distributed MIMO based RAN was presented.Then,enhanced interference coordination(e IC)was described.Finally,the effectiveness of distributed MIMO based RAN with e IC by computer simulation was showed.

A DYNAMIC SPECTRUM ACCESS NETWORK BASED ON COGNITIVE RADIO

Dynamic spectrum access technologies based on Cognitive Radio(CR) is under intensive research carried out by the wireless communication society and is expected to solve the problem of spectrum scarcity.However,most enabling technologies related to dynamic spectrum access are con-sidered individually.In this paper,we consider these key technologies jointly and introduce a new implementation scheme for a Dynamic Spectrum Access Network Based on Cognitive Radio(DSAN-BCR).We start with a flexible hardware platform for DSAN-BCR,as well as a flexible protocol structure that dominates the operation of DSAN-BCR.We then focus on the state of the art of key technologies such as spectrum sensing,spectrum resources management,dynamic spectrum access,and routing that are below the network layer in DSAN-BCR,as well as the development of technologies related to higher layers.Last but not the least,we analyze the challenges confronted by these men-tioned technologies in DSAN-BCR,and give the perspectives on the future development of these technologies.The DSAN-BCR introduced is expected to provide a system level guidance to alleviate the problem of spectrum scarcity.

Evaluate mobile video quality with LTE radio access network parameters

To evaluate the video quality, we tested sample videos delivered using HTTP adaptive streaming （HAS） in LTE network. In order to establish a correlation between radio access network （RAN） performance and quality of experience （ QoE）, we set up a testbed under different radio im- pairment conditions with three parameters： signal to interference and noise ratio （ SINR）, an amount of available network resource and a round trip latency. End users graded each video in a mobile equipment with their QoE Mearnwhile, we used a nonlinear model to simulate the comprehensive pre- dicted mean opinion score （pMOS）. Our results show that the nonlinear model can predict the enduser＇ s feedback. The pearson correlation coefficient （PCC） of the model is larger than 0. 9. This demonstrate that the output of the model has a high correlation with the end users＇ ratings and can reflect the QoE accurately. The method we developed will help mobile network operators evaluate the RAN performance of its QoE. It can also be used for HAS service to optimize LTE network and improve its QoE.

A Broad Learning-Driven Network Traffic Analysis System Based on Fog Computing Paradigm

The development of communication technologies which support traffic-intensive applications presents new challenges in designing a real-time traffic analysis architecture and an accurate method that suitable for a wide variety of traffic types.Current traffic analysis methods are executed on the cloud,which needs to upload the traffic data.Fog computing is a more promising way to save bandwidth resources by offloading these tasks to the fog nodes.However,traffic analysis models based on traditional machine learning need to retrain all traffic data when updating the trained model,which are not suitable for fog computing due to the poor computing power.In this study,we design a novel fog computing based traffic analysis system using broad learning.For one thing,fog computing can provide a distributed architecture for saving the bandwidth resources.For another,we use the broad learning to incrementally train the traffic data,which is more suitable for fog computing because it can support incremental updates of models without retraining all data.We implement our system on the Raspberry Pi,and experimental results show that we have a 98%probability to accurately identify these traffic data.Moreover,our method has a faster training speed compared with Convolutional Neural Network(CNN).

Power Allocation for Video Segment Based Caching Strategy in F-RAN Architecture

The concept of edge network caching has been proposed to alleviate the excessive pressure on the core networks.Furthermore,video segment caching technology,a method to cut the whole video into segments and cache them separately,has brought a novel idea to solve the caching problem in the smaller space for massive data.The adoption of segment caching in edge networks will divide the simple video transmission process into two coupling stages because of separate data caching,which leads to more complicated resource allocation.In this paper,this problem is discussed,and its mathematical model is established to minimize the energy consumption of video transmissions.By introducing an efficient prediction window of channel fading,an optimal dynamic scheduling algorithm based on Qlearning is proposed to minimize power consumption while ensuring smooth video streaming.The proposed Q-learning algorithm is simulated and the impacts of channel state,target video bit rate and largescale channel parameter are evaluated.Simulation results show that the proposed method can effectively reduce the total power consumption while ensuring the smooth playback of video service,thanks to the fact that the proposed method is intelligent which can effectively utilize idle resources in favorable channel states.

Green Concerns in Federated Learning over 6G

As Information,Communications,and Data Technology(ICDT)are deeply integrated,the research of 6G gradually rises.Meanwhile,federated learning(FL)as a distributed artificial intelligence(AI)framework is generally believed to be the most promising solution to achieve“Native AI”in 6G.While the adoption of energy as a metric in AI and wireless networks is emerging,most studies still focused on obtaining high levels of accuracy,with little consideration on new features of future networks and their possible impact on energy consumption.To address this issue,this article focuses on green concerns in FL over 6G.We first analyze and summarize major energy consumption challenges caused by technical characteristics of FL and the dynamical heterogeneity of 6G networks,and model the energy consumption in FL over 6G from aspects of computation and communication.We classify and summarize the basic ways to reduce energy,and present several feasible green designs for FL-based 6G network architecture from three perspectives.According to the simulation results,we provide a useful guideline to researchers that different schemes should be used to achieve the minimum energy consumption at a reasonable cost of learning accuracy for different network scenarios and service requirements in FL-based 6G network.

Mobility Management in Small Cell Cluster of Cellular Network

The installation of small cells in a 5G network extends the maximum coverage and provides high availability.However,this approach increases the handover overhead in the Core Network(CN)due to frequent handoffs.The variation of user density and movement inside a region of small cells also increases the handover overhead in CN.However,the present 5G system cannot reduce the handover overhead in CN under such circumstances because it relies on a traditionally rigid and complex hierarchical sequence for a handover procedure.Recently,Not Only Stack(NO Stack)architecture has been introduced for Radio Access Network(RAN)to reduce the signaling during handover.This paper proposes a system based on NO Stack architecture and solves the aforementioned problem by adding a dedicated local mobility controller to the edge cloud for each cluster.The dedicated cluster controller manages the user mobility locally inside a cluster and also maintains the forwarding data of a mobile user locally.To reduce the latency for X2-based handover requests,an edge cloud infrastructure has been also developed to provide high-computing for dedicated controllers at the edge of a cellular network.The proposed system is also compared with the traditional 3GPP architecture and other works in the context of overhead and delay caused by X2-based handover requests during user mobility.Simulated results show that the inclusion of a dedicated local controller for small clusters together with the implementation of NO Stack framework reduces the significant amount of overhead of X2-based handover requests at CN.

Software-Defined Cellular Mobile Network Solutions

The emergency relating to software-defined networking （SDN）, especially in terms of the prototype associated with OpenFlow, provides new possibilities for innovating on network design. Researchers have started to extend SDN to cellular networks. Such new programmable architecture is beneficial to the evolution of mobile networks and allows operators to provide better services. The typical cellular network comprises radio access network （RAN） and core network （CN）; hence, the technique roadmap diverges in two ways. In this paper, we investigate SoftRAN, the latest SDN solution for RAN, and SoftCell and MobileFlow, the latest solutions for CN. We also define a series of control functions for CROWD. Unlike in the other literature, we emphasize only softwaredefined cellular network solutions and specifications in order to provide possible research directions.

User Association in Ultra-Dense Small Cell Dynamic Vehicular Networks: A Reinforcement Learning Approach

Network densification is envisioned as one of the key enabling technologies in the next generation and beyond wireless networks to satisfy the demand of high coverage and capacity whilst deliver an ultra-reliable low latency communication services especially to the users on the move.One of the fundamental tasks in wireless networks is user association.In the case of ultra-dense vehicular networks,due to the dense deployment and small coverage of the eNodeBs,there may be more than one eNodeB that may simultaneously satisfy the conventional maximum radio signal strength user association criteria.In addition to this,the spatial-temporal vehicle distribution in dynamic environments contribute significantly towards the rapidly changing radio environment that substantially impacts the user association,therefore,the network performance and user experience.This paper addresses the problem of user association in dynamic environments by proposing intelligent user association approach,variable-reward,quality-aware Q-learning(VR-QAQL)that has an ability to strike a balance between the number of handovers per transmission and system performance whilst a guaranteed network quality of service is delivered.The VR-QAQL technique integrates the control-theoretic concepts and the reinforcement learning approach in an LTE uplink,using the framework of an urban vehicular environment.The algorithm is assessed using large-scale simulation on a highway scenario at different vehicle speeds in an urban setting.The results demonstrate that the proposed VR-QAQL algorithm outperforms all the other investigated approaches across all mobility levels.

An intelligent self-sustained RAN slicing framework for diverse service provisioning in 5G-beyond and 6G networks

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,the self-optimization of slicing control performance,and self-learning together to achieve an adaptive control strategy under unforeseen network conditions.The proposed RAN slicing framework is hierarchically structured,which decomposes the RAN slicing control into three levels,i.e.,network-level slicing,next generation NodeB(gNodeB)-level slicing,and packet scheduling level slicing.At the network level,network resources are assigned to each gNodeB at a large timescale with coarse resource granularity.At the gNodeB-level,each gNodeB adjusts the configuration of each slice in the cell at the large timescale.At the packet scheduling level,each gNodeB allocates radio resource allocation among users in each network slice at a small timescale.Furthermore,we utilize the transfer learning approach to enable the transition from a model-based control to an autonomic and self-learning RAN slicing control.With the proposed RAN slicing framework,the QoS performance of emerging services is expected to be dramatically enhanced.

Resource allocation for network profit maximization in NOMA-based F-RANs:a game-theoretic approach

Non-orthogonal multiple access(NOMA)based fog radio access networks(F-RANs)offer high spectrum efficiency,ultra-low delay,and huge network throughput,and this is made possible by edge computing and communication functions of the fog access points(F-APs).Meanwhile,caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase,which facilitates further reduction in the transmission energy and burden.The need of the prevailing situation in industry is that in NOMA-based F-RANs,energy-efficient resource allocation,which consists of cache placement(CP)and radio resource allocation(RRA),is crucial for network performance enhancement.To this end,in this paper,we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol.Then,we formulate a resource allocation problem for maximizing the defined performance indicator,namely network profit,which takes caching cost,revenue,and energy efficiency into consideration.The NP-hard problem is decomposed into two sub-problems,namely the CP sub-problem and RRA sub-problem.Finally,we propose an iterative method and a Stackelberg game based method to solve them,and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.