Multi-Radio Cooperation Technology and Heterogeneous Network Convergence

The heterogeneous network convergence is the trend of future network development. However, many bottlenecks exist in the converged system such as high time delay, high energy consumption, and low data rate. Multi-Radio Cooperation (MRC) technology is specially designed to overcome these bottlenecks and to satisfy the requirements of heterogeneous network convergence. MRC can improve network capacity, reduce the energy consumption of radio devices, and decrease handover latency between heterogeneous networks by the cooperation of multiple radios, and by efficient management and feasible allocation of multi-radio resources. MRC makes real seamless heterogeneous network convergence possible.

Network Convergence and Service Convergence

Convergence is the main trend for future network development. The multi-service bearer network,built upon the Internet Protocol/Multi-protocol Label Switching (IP/MPLS) technology,is a converged network with Quality of Service (QoS) guarantee for business and enterprise customers. The integrated and unified service provisioning platform (including both service execution and development enviroments) can provide integrated audio,video and data services over multiple access networks. However,achieving network convergence and service convergence are long-term goals,which can only be realized step by step after comprehensive consideration of many factors like market demands,technologies,services and costs.

The Games in Network Convergence and Their Impact on China

In January 2010,the State Council of China decided to speed up the convergence of the telecommunications network,cable network and Internet network.That means the government-led practical exploration of the networks convergence.The convergence is related to the entities in the fields of telecommunications,broadcasting and the Internet industry.The authors apply the static game theory to analyze the games among the players in the fields of telecommunications and broadcasting,and point out that the games would strengthen the network infrastructures,promote the development of related industries,and profoundly influence and change the ways of thinking and social management in China.At last,the paper puts forward some policy recommendations.

Trends and Prospects of Network Convergence

The concept of the separation of service,control,bearer and access in the Next Generation Network (NGN) provides a unified architecture for the network convergence in the future. Network convergence involves service,core network,access network,terminal,and operation and maintenance convergence. In the future,the converged network’s trend is to use IP Multimedia Subsystem (IMS) at the core control layer; Internet Protocol/Multiple Protocol Label Switching (IP/MPLS) technology at the core bearer layer; unified and open service provisioning architecture at the service layer; as well as support fixed,mobile,narrowband and broadband access technologies and so on at the access layer. Terminals will evolve to be multi-modular and intelligent,and would finally enable users to access full services anytime and anywhere using one number,one terminal,one account and one core network.

Study on QoS and Communications Capacity in Heterogeneous Network Convergence

To provide any subscriber from anywhere at anytime with services that have both secured Quality of Service(QoS) and simultaneous expansion of network coverage and communications capacity is a key problem that has to be considered and solved in heterogeneous network convergence.Key technologies for a secured QoS and communications capacity analysis under heterogeneous environment are important subjects for research.Key technologies for a secured QoS are mainly on radio resource management algorithms covering Call Admission Control(CAC) algorithm,vertical handover algorithm,heterogeneous resource allocation algorithm and network selection algorithm.The applications of a novel multi-hop in heterogeneous convergence system serve the purposes of network coverage expansion,transmission power reduction,system communication capacity and throughput increase.

Mesh-Based Network Convergence and Cooperation

More and more distinctive heterogeneous features are exhibited in wireless networks in multitude of networks, technologies, terminals, operation and management, and more. Effective convergence and cooperation of heterogeneous networks can be achieved through Mesh technology. The IP-based convergence integrates common features of heterogeneous networks, while the cooperation coordinates personalities of the networks. By using the convergence and cooperation, a standardized integration of separated and localized predominant capabilities and resources of heterogeneous networks can be fulfilled to enable the evolution into an ubiquitous and omnipotent intelligent network.

Communication efficiency optimization of federated learning for computing and network convergence of 6G networks

Federated learning effectively addresses issues such as data privacy by collaborating across participating devices to train global models.However,factors such as network topology and computing power of devices can affect its training or communication process in complex network environments.Computing and network convergence(CNC)of sixth-generation(6G)networks,a new network architecture and paradigm with computing-measurable,perceptible,distributable,dispatchable,and manageable capabilities,can effectively support federated learning training and improve its communication efficiency.By guiding the participating devices'training in federated learning based on business requirements,resource load,network conditions,and computing power of devices,CNC can reach this goal.In this paper,to improve the communication eficiency of federated learning in complex networks,we study the communication eficiency optimization methods of federated learning for CNC of 6G networks that give decisions on the training process for different network conditions and computing power of participating devices.The simulations address two architectures that exist for devices in federated learning and arrange devices to participate in training based on arithmetic power while achieving optimization of communication efficiency in the process of transferring model parameters.The results show that the methods we proposed can cope well with complex network situations,effectively balance the delay distribution of participating devices for local training,improve the communication eficiency during the transfer of model parameters,and improve the resource utilization in the network.

Computing Power Network:The Architecture of Convergence of Computing and Networking towards 6G Requirement

In 6G era,service forms in which computing power acts as the core will be ubiquitous in the network.At the same time,the collaboration among edge computing,cloud computing and network is needed to support edge computing service with strong demand for computing power,so as to realize the optimization of resource utilization.Based on this,the article discusses the research background,key techniques and main application scenarios of computing power network.Through the demonstration,it can be concluded that the technical solution of computing power network can effectively meet the multi-level deployment and flexible scheduling needs of the future 6G business for computing,storage and network,and adapt to the integration needs of computing power and network in various scenarios,such as user oriented,government enterprise oriented,computing power open and so on.

Service Convergence in Next Generation Network

The convergence of communication services becomes a focus in the industry along with the requirement for full-service operation and technical development. Service convergence includes two aspects: The convergence of the fixed and mobile networks; and the convergence of traditional communications and Internet services. This requires balancing the conflicts between the openness and operationability of terminal capability and network convergence. Unified authentication and authorization are the basis for service convergence in terms of operationability. Modular network and open terminal are technical solutions for the service convergence.

The Convergence of WLAN and CDMA 2000 1x Networks

The technical solutions to the convergence of WLAN and CDMA 2000 1x are presented and compared.Problems brought on by network convergence are analyzed,and solutions to problems such as the coexistence of packet data service nodes(PDSNs),user i- dentification,roaming,and handoff are discussed in detail.

Satellite Communications Integration with Terrestrial Networks

In this paper we evaluate the feasibility of seamless and efficient integration of terrestrial communication systems with satellite networks. This aspect is considered by standardisation bodies such as ETSI [1], 5 G-PPP [2] and 3 GPP [3]. A comprehensive system is designed and implemented in an emulation prototype, including standard 3 GPP LTE core network functionality [4] with its different layers: networking, data forwarding, control, management and monitoring and is validated through performance measurements. This work is a technical feasibility study of extending terrestrial communication systems with satellite networks as backhaul, increasing the energy efficiency, network robustness during natural disasters as well as being an alternative for peak-time data forwarding of the terrestrial communication services. Due to its global coverage property, terrestrial-satellite integration provides an obvious extension of communication services towards isolated and remote areas and an alternative for rural or highly distributed/highly mobile enterprise networks.

Cognition Flow in Cognitive Radio Networks

This paper introduces the research progress on the cognition flow in the National Basic Research Program of China(973 Program)under Grant No. 2009CB320400 "Research on Basic Theories and Key Technologies of Cognitive Radio Networks". We present the motivation behind the proposal of the concept of cognition flow, provide the definition, discuss the features, behaviours, representations, mathematical models, and the functions of cognition flow in Cognitive Radio Networks(CRNs). We also analyse how the cognition flow promotes the convergence of heterogeneous networks.Our group also constructed the test platform to verify the usefulness of cognition flow. The results that were simulated by computers and tested on the platform both confirm that cognition flow can realise efficient interaction of cognitive information among heterogeneous networks in CRNs, which contributes to the seamless convergence of heterogeneous networks,and significantly improves the spectrum efficiency and users' Quality of Experience(QoE).

QoS Issues in Converged Networks

The convergence of telecom networks requires an improved Quality of Service (QoS) solution. The introduction of the QoS resource control architecture based on the IP Multimedia Subsystem (IMS) framework is a way to solve QoS problems in the converged networks. Services,QoS guarantee,resource control and accounting functions are integrated into this IMS-based QoS control architecture. It is a solution to the problems currently facing the fixed network operators,helping them offer more QoS-guaranteed multimedia services such as 3D games,and make a transition from bandwidth providers to genuine telecom services providers.

IMS and Converged Service Network

The current telecom target network architecture with IP Multimedia Subsystem (IMS) as the core lacks the actual verification of existing large-scale networks and faces the stiff competition from Internet technologies. In the process of promoting the commercial usage of IMS services, the Service Delivery Platform (SDP) is evolving into a more convergent and open Service Delivery Environment (SDE) and will become the mainstream of the next generation Telecom Service Network. ZTE’s concept of the Converged Service Network (CSN) is to integrate various network service capabilities to realize the unification of data, service, capability and management and thus the fast provisioning of various Value-Added Services (VASs), which helps the carriers to maintain a leading position in the value chain, meeting the telecom carriers ’ requirements for service network development.

A Software Defined Open Wi-Fi Platform

Wireless local area network(WLAN) is an indivisible part of the next generation wireless system. In this paper, an open Wi-Fi platform is designed and developed with special consideration of real-time signal processing. Such system can help accelerate research and development of future wireless network, especially in the case of cellular/Wi-Fi co-existing networks. This platform is based on the Intel general-purpose processor and the universal software radio peripheral(USRP) radio front end. The design including the physical layer implementations is purely software and is optimized for real-time signal processing on the general purpose processor. In the lab experiment, this platform supports baseband rate up to 700 Mbps with 2 transmitters in 80 MHz bandwidth. A cellular-Wi-Fi signaling interface between the Wi-Fi access point(AP) and the 5G core network is also developed and validated as an example for wireless resource allocation.

Wireless Communication and Broadcasting Convergence Network Throughput

Wireless communication and broadcasting convergence networks provide a potential solution to greater traffic throughput in the future. In this paper, the throughput of a convergence network is analyzed based on the standardization project for specific requirements for local and metropolitan area networks in China, referred to as broadband wireless multimedia systems. The convergence network is modeled as a combination of a broadcasting channel and a multi-access channel with interference. The throughput is then given as a function of the time resource allocation by calculating the channel capacity with interference. The maximum throughput and the optimal time resource allocation are then determined for a given delay constraint and traffic requirements. The results give guidelines for time resource allocation and system design for convergence networks.

Neural Networks with Local Converging Inputs(NNLCI)for Solving Conservation Laws,Part I:1D Problems

A novel neural network method is developed for solving systems of conservation laws whose solutions may contain abrupt changes of state,including shock waves and contact discontinuities.In conventional approaches,a low-cost solution patch is usually used as the input to a neural network for predicting the high-fidelity solution patch.With that technique,however,there is no way to distinguish a smeared discontinuity from a smooth solution with large gradient in the input,and the two almost identical inputs correspond to two fundamentally different high-fidelity solution patches in training and predicting.To circumvent this difficulty,we use local patches of two low-cost numerical solutions of the conservation laws in a converging sequence as the input to a neural network.The neural network then makes a correct prediction by identifying whether the solution contains discontinuities or just smooth variations with large gradients,because the former becomes increasingly steep in a converging sequence in the input,and the latter does not.The inputs can be computed from lowcost numerical schemes with coarse resolution,in a local domain of dependence of a space-time location where the prediction is to be made.Despite smeared input solutions,the output provides sharp approximations of solutions containing shock waves and contact discontinuities.The method works effectively not only for regions with discontinuities,but also for smooth regions of the solution.It is efficient to implement,once trained,and has broader applications for different types of differential equations.

Neural Networks with Local Converging Inputs(NNLCI)for Solving Conservation Laws,Part II:2D Problems

In our prior work[10],neural networks with local converging inputs(NNLCI)were introduced for solving one-dimensional conservation equations.Two solutions of a conservation law in a converging sequence,computed from low-cost numerical schemes,and in a local domain of dependence of the space-time location,were used as the input to a neural network in order to predict a high-fidelity solution at a given space-time location.In the present work,we extend the method to twodimensional conservation systems and introduce different solution techniques.Numerical results demonstrate the validity and effectiveness of the NNLCI method for application to multi-dimensional problems.In spite of low-cost smeared input data,the NNLCI method is capable of accurately predicting shocks,contact discontinuities,and the smooth region of the entire field.The NNLCI method is relatively easy to train because of the use of local solvers.The computing time saving is between one and two orders of magnitude compared with the corresponding high-fidelity schemes for two-dimensional Riemann problems.The relative efficiency of the NNLCI method is expected to be substantially greater for problems with higher spatial dimensions or smooth solutions.

Enhancement network: confining deep convolutional features based on hand-crafted rules

Despite convolutional neural network(CNN) is mature in many domains, the understanding of the directions where the parameters of the CNNs are learned towards, falls behind, and researches on the functions that the convolutional networks(ConvNets) learns are difficult to be explored. A method is proposed to guide ConvNets to learn towards the expected direction. First, for the sake of facilitating network converging, a novel feature enhancement framework, namely enhancement network(EN), is devised to learn parameters according to certain rules. Second, two types of hand-crafted rules, namely feature-sharpening(FS) and feature-amplifying(FA) are proposed to enable effective ENs, meanwhile are embedded into the CNN for the end-to-end learning. Specifically, the former is a tool sharpening convolutional features and the latter is the one amplifying convolutional features linearly. Both tools aim at the same spot achieving a stronger inductive bias and more straightforward loss functions. Finally, the experiments are conducted on the mixed National Institute of Standards and Technology(MNIST) and cooperative institute for Alaska research 10(CIFAR10) dataset. Experimental results demonstrate that ENs make a faster convergence by formulating hand-crafted rules.

Cooperative localization for next-generation converged networks with closed-form solution

In this article, a novel cooperative wireless localization scheme based on information fusion is proposed. The scheme combines large-scale arrival time and small-scale distance measurements obtained from the next-generation converged networks. The maximum likelihood （ML） estimate of the terminal＇s position is derived with closed-form solution, and the Cramer-Rao lower bound （CRLB） of the estimate error is investigated. Both theoretical analysis and simulation results verify that the proposed localization scheme can significantly enhance the location precision. Moreover, the mean square error of position estimate approximates the CRLB when the number of reference stations increases, which indicates that the proposed ML estimator is asymptotically efficient.