An analysis on domestic users' awareness towards Open Access resources:A case study of university users in Nanjing

Based on a survey of university teachers and students in Nanjing area of 2007,this article analyzes domestic users' awareness towards Open Access(OA),and the changes by comparing the corresponding data of 2006.The survey shows that the research evaluation system,the quality of literature and the payment for OA publications are the important factors for university users to choose OA or not.The authors finally suggest that the current research evaluation system should be reformed to accommodate OA journals.Meanwhile,great efforts should be devoted to improve the quality of OA literatures,and the governments and research institutions should provide stronger support than before.

Resource Control Architecture in Heterogeneous Networks

TISPAN,from a fixed access perspective,proposes Resource and Admission Control Subsystem[0](RACS) as a solution to Quality of Service(QoS) problem for NGN bearer network.In contrast,3GPP has an approach to this from the perspective of mobile access.In the latest 3GPP R7 draft,integration of Policy Control Function(PCF) with Flow Based Charging(FBC) function of the R6 brought forward policy control and charging.With the development of fixed mobile convergence,the inconsistence in architectures and interfaces of different resource and admission control[0] solutions will have a huge impact on manufacture and network implementation of NGN related equipment.To solve this problem,both 3GPP and TISPAN have been working on the convergence of Gq’/Rx reference points.Harmonized Policy Control and Charging(PCC) proposed by the Next Generation Mobile Network(NGMN) forum,i.e.cooperative resource control architecture for heterogeneous networks,represents an evolutional sign post for resource control technology for heterogeneous network architecture.

Identifying Industrial Control Equipment Based on Rule Matching and Machine Learning

To identify industrial control equipment is often a key step in network mapping,categorizing network resources,and attack defense.For example,if vulnerable equipment or devices can be discovered in advance and the attack path canbe cut off,security threats canbe effectively avoided and the stable operationof the Internet canbe ensured.The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability.This paper proposes an industrial control device identification method based on PCA-Adaboost,which integrates rule matching and machine learning.We first build a rule base from network data collection and then use single andmulti-protocol rule-matchingmethods to identify the type of industrial control devices.Finally,we utilize PCA-Adaboost to identify unlabeled data.The experimental results show that the recognition rate of this method is better than that of the traditional Nmap device recognitionmethod and the device recognition accuracy rate reaches 99%.The evaluation effect of the test data set is significantly enhanced.

Integrated Sensing and Communication Enabled Multiple Beamwidth and Power Allocation for Connected Automated Vehicles

Connected autonomous vehicles(CAVs)are a promising paradigm for implementing intelligent transportation systems.However,in CAVs scenarios,the sensing blind areas cause serious safety hazards.Existing vehicle-to-vehicle(V2V)technology is difficult to break through the sensing blind area and ensure reliable sensing information.To overcome these problems,considering infrastructures as a means to extend the sensing range is feasible based on the integrated sensing and communication(ISAC)technology.The mmWave base station(mmBS)transmits multiple beams consisting of communication beams and sensing beams.The sensing beams are responsible for sensing objects within the CAVs blind area,while the communication beams are responsible for transmitting the sensed information to the CAVs.To reduce the impact of inter-beam interference,a joint multiple beamwidth and power allocation(JMBPA)algorithm is proposed.By maximizing the communication transmission rate under the sensing constraints.The proposed non-convex optimization problem is transformed into a standard difference of two convex functions(D.C.)problem.Finally,the superiority of the lutions.The average transmission rate of communication beams remains over 3.4 Gbps,showcasing a significant improvement compared to other algorithms.Moreover,the satisfaction of sensing services remains steady.

Reconnectable Network with Limited Resources

The reachability of a strongly connected network may be destroyed after link damage.Since many networks are directed or equivalent directed,connected by directed links with the potential for reversal. Therefore the reachability can be restored by reversing the direction of links.[1]has studied this matter under unlimited resources(transmitter and receiver)condition.In this paper the reconnectability of a net- work with limited number of receivers and transmitters is discussed.Also a linear time algorithm is given to find a reconnected reversal for limited receivers and transmitters.

COLLABORATIVE RESOURCE ALLOCATION OVER A HYBRID CLOUD CENTER AND EDGE SERVER NETWORK

This paper considers the collaborative resource allocation problem over a hybrid cloud center and edge server network, an emerging infrastructure for efficient Internet services. The cloud center acts as a pool of inexhaustible computation and storage powers. The edge servers often have limited computation and storage powers but are able to provide quick responses to service requests from end users. Upon receiving service requests, edge servers assign them to themselves, their neighboring edge servers, as well as the cloud center, aiming at minimizing the overall network cost. This paper first establishes an optimization model for this problem. Second, in light of the separable structure of the optimization model, we utilize the alternating direction method of multipliers （ADMM） to develop a fully collaborative resource allocation algorithm. The edge servers and the cloud center autonomously collaborate to compute their local optimization variables and prices of network resources, and reach an optimal solution. Numerical experiments demonstrate the effectiveness of the hybrid network infrastructure as well as the proposed algorithm.

An enhanced framework for providing multimedia broadcast/multicast service over heterogeneous networks

Multimedia broadcast multicast service(MBMS)with inherently low requirement for network resources has been proposed as a candidate solution for using such resources in a more efficient manner.On the other hand,the Next Generation Mobile Network(NGMN)combines multiple radio access technologies(RATs)to optimize overall network performance.Handover performance is becoming a vital indicator of the quality experience of mobile user equipment(UE).In contrast to the conventional vertical handover issue,the problem we are facing is how to seamlessly transmit broadcast/multicast sessions among heterogeneous networks.In this paper,we propose a new network entity,media independent broadcast multicast service center(MIBM-SC),to provide seamless handover for broadcast/multicast sessions over heterogeneous networks,by extensions and enhancements of MBMS and media independent information service(MIIS)architectures.Additionally,a network selection scheme and a cell transmission mode selection scheme are proposed for selecting the best target network and best transmission mode.Both schemes are based on a load-aware network capacity estimation algorithm.Simulation results show that the proposed approach has the capability to provide MBMS over heterogeneous networks,with improved handover performance in terms of packet loss rate,throughput,handover delay,cell load,bandwidth usage,and the peak signal-to-noise ratio(PSNR).

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.

Multi-radio access based bandwidth allocation strategy for video communication in heterogeneous wireless networks

In order to make full use of the radio resource of heterogeneous wireless networks（HWNs） and promote the quality of service（Qo S） of multi-homing users for video communication, a bandwidth allocation algorithm based on multi-radio access is proposed in this paper. The proposed algorithm adopts an improved distributed common radio resource management（DCRRM） model which can reduce the signaling overhead sufficiently. This scheme can be divided into two phases. In the first phase, candidate network set of each user is obtained according to the received signal strength（RSS）. And the simple additive weighted（SAW） method is employed to determine the active network set. In the second phase, the utility optimization problem is formulated by linear combining of the video communication satisfaction model, cost model and energy efficiency model. And finding the optimal bandwidth allocation scheme with Lagrange multiplier method and Karush-Kuhn-Tucker（KKT） conditions. Simulation results show that the proposed algorithm promotes the network load performances and guarantees that users obtain the best joint utility under current situation.

Energy efficiency enhancement in heterogeneous networks:a joint resource allocation approach

To support the drastic growth of wireless multimedia services and the requirements of ubiquitous access, numerous wireless infrastructures which consume enormous energy, such as macrocell, small cell, distributed antenna systems and wireless sensor networks, have been deployed. Under the background of environmental protection, improving the energy efficiency（EE） in wireless networks is becoming more and more important. In this paper, an EE enhancement scheme in heterogeneous networks（Het Nets） by using a joint resource allocation approach is proposed. The Het Nets consists of a mix of macrocell and small cells. Firstly, we model this strategic coexistence as a multi-agent system in which decentralized resource management inspired from Reinforcement Learning are devised. Secondly, a Q-learning based joint resource allocation algorithm is designed. Meanwhile, with the consideration of the time-varying channel characteristics, we take the long-term learning reward into account. At last, simulation results show that the proposed decentralized algorithm can approximate to centralized algorithm with low-complexity and obtain high spectral efficiency（SE） in the meantime.

Genetic algorithm for autonomic joint radio resource management in end-to-end reconfigurable systems

This article presents the genetic algorithm （GA） as an autonomic approach for the joint radio resource management （JRRM） amongst heterogeneous radio access technologies （RATs） in the end-to-end reconfigurable systems. The joint session admission control （JOSAC） and the bandwidth allocation are combined as a specific decision made by the operations of the genetic algorithm with certain advisable modifications. The proposed algorithm is triggered on the following two conditions When a session is initiated, it is triggered for the session to camp on the most appropriate RAT and select the most suitable bandwidth for the desired service. When a session terminates, it is also used to adjust the distribution of the ongoing sessions through the handovers. This will increase the adjustment frequency of the JRRM controller for the best system performance. Simulation results indicate that the proposed autonomic JRRM scheme not only effectively reduces the handover times, but also achieves well trade-off between the spectrum utility and the blocking probability.

Resource scheduling virtualization in service-oriented future Internet architecture

Service-oriented future internet architecture（SOFIA） is a clean-slate network architecture. In SOFIA, a service request is mainly processed through service resolution and network resource allocation. To realize the network resource allocation, we reference the idea of network virtualization and propose resource scheduling virtualization. In resource scheduling virtualization, a service request is abstracted as a virtual network（VN） and the network resources are allocated by mapping the VN onto the physical network. Resource scheduling virtualization provides centralized resource scheduling control within an autonomous system（AS） and achieves better controllability compared with the distributed schemes. Besides, resource scheduling virtualization supports multi-site selection as well. Meanwhile, we propose a collection of resource scheduling algorithms based on maximum resource tree（MRT） adapting to different scenarios. According to the simulation results, the proposed algorithms show good performance on the key metrics, such as acceptance ratio, revenue, cost and utilization. Moreover, the simulation results reveal that our algorithm is more efficient than the traditional ones.