An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks isskyrocketing due to the rapid advancement of sensors and wireless communicationtechnology. In the upcoming years, it is anticipated that mobile datatraffic would rise even more. The development of a new cellular networkparadigm is being driven by the Internet of Things, smart homes, and moresophisticated applications with greater data rates and latency requirements.Resources are being used up quickly due to the steady growth of smartphonedevices andmultimedia apps. Computation offloading to either several distantclouds or close mobile devices has consistently improved the performance ofmobile devices. The computation latency can also be decreased by offloadingcomputing duties to edge servers with a specific level of computing power.Device-to-device (D2D) collaboration can assist in processing small-scaleactivities that are time-sensitive in order to further reduce task delays. The taskoffloading performance is drastically reduced due to the variation of differentperformance capabilities of edge nodes. Therefore, this paper addressed thisproblem and proposed a new method for D2D communication. In thismethod, the time delay is reduced by enabling the edge nodes to exchangedata samples. Simulation results show that the proposed algorithm has betterperformance than traditional algorithm.

Resource Allocation Based on SFLA Algorithm for D2D Multicast Communications

Multicast device-to-device(D2D)communication technology is considered as one of the new technologies in the fifth generation(5G)networks that directly addresses the need for content sharing among internet users.In fact,when direct communication is available between devices,the spectral efficiency is improved by reusing the licensed cellular spectrum.The current studies show that D2D communication increases network capacity and reduces latency.In order to achieve the alternate capabilities,coordination is required to implement interference management.We considered subcarrier allocation for the uplink,in addition to the power control that takes place on the underlay network.The completed data rate in single multicast communication is significantly reduced and limited by nodes with lower channel quality.In this paper,we used Shuffled Frog Leaping Algorithm(SFLA)for resource allocation(RA)in D2D multicast communications.We compared the results of the SFLA algorithm with the Firefly Algorithm(FA),Ant Colony Optimization(ACO)and Particle Swarm Optimization(PSO);in terms of D2D user throughput,Cellular User(CU)throughput,network average throughput,network interference and signal interference noise ratio(SINR)target.The simulation results show that SFLA clearly outperforms other algorithms in terms of data rate under the high pressure of infeasibility.

Design of Semipersistent Resource Allocation in LTE-V Network

Radio network access technology currently used in 4G/5G is Long Term Evolution-Advanced(LTE-A),which was developed by 3rd Generation Partnership Project(3GPP).Device-to-device(D2D)communication is a technology enabling direct communications among wireless devices without forwarding through an evolved Node B(eNB).Moreover,D2D transmission can support vehicles as a vehicle-to-vehicle(V2V)environment.It is possible to avoid accidents via exchanging movement-related information among vehicles and effectively increase driving safety(and efficiency).However,radio resources are limited in radio networks.A vehicle transmits through D2D in Long Term Evolution-Vehicle(LTE-V)mode-3 standard,and an eNB can allocate the same spectrum radio resources for cellular and V2V links simultaneously.When using the same radio resources,the probability of interference may increase.This study designed a semipersistent resource allocation algorithm based on different cycles in an LTE-V network.Moreover,resource allocation under different cycles was analyzed,and a scheme for resource selection is proposed based on cycle size.The proposed Semi Persistent Gain Aware Resource Allocation(SP-GARA)scheme selects resources based on the expected sum rate of the cycle size and analyzes and discusses the results of the total sum rate at different cycles and speeds for an improved performance.

Resource allocation method for device-to-device communications in cellular networks

Based on the conflict graph model which is formulated as a binary integer optimization problem, a resource allocation method to support device-to-device （D2D） communications in ceUular networks is proposed. First, a frequency resource assignment algorithm is presented which assigns each D2D link one frequency resource block. For this algorithm, frequency resource blocks are assigned so that the frequency resource spatial reuse opportunities in the cellular networks can be fully exploited. Then a slot scheduling algorithm is presented which schedules time slots among D2D links assigned the same frequency resource block. For this algorithm, time slot resources are scheduled so that the proportional fairness among D2D links which are assigned the same frequency resource block can be achieved. The performance of the proposed method is evaluated via computer simulations. The simulation results show that the proposed method can well support D2D communications in cellular networks.

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device(D2D)communication which is also called terminal pass-through technology.It can directly communicate between devices under the control of a base station and does not require a base station to forward it.The advantages of applying D2D communication technology to cellular networks are:It can increase the communication system capacity,improve the system spectrum efficiency,increase the data transmission rate,and reduce the base station load.Aiming at the problem of co-channel interference between the D2D and cellular users,this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning,which creates multi-agent learners from multiple D2D users,and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users.The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted.The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users.Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.

Energy-Efficient Resource Allocation for Heterogeneous Network with Grouping D2D

In this paper,a new communication model is built named grouping D2D(GD2D).Different from the traditional D2D coordination,we proposed GD2D communication in licensed and unlicensed spectrum simultaneously.We formulate a resource allocation problem,which aims at maximizing the energy efficiency(EE)of the system while guaranteeing the quality-of-service(Qos)of users.To efficiently solve this problem,the non-convex optimization problem is first transformed into a convex optimization problem.By transforming the fractional-form problem into an equivalent subtractive-form problem,an iterative power allocation algorithm is proposed to maximize the system EE.Moreover,the optimal closedform power allocation expressions are derived by the Lagrangian approach.Simulation results show that our algorithm achieves higher EE performance than the traditional D2D communication scheme.

Energy efficient resource allocation for D2D multicast communications

The resource allocation for device-to-device(D2D)multicast communications is investigated.To achieve fair energy efficiency(EE)among different multicast groups,the max-min fairness criterion is used as the optimization criterion and the EE of D2D multicast groups are taken as the optimization objective function.The aim is to maximize the minimum EE for different D2D multicast groups under the constraints of the maximum transmit power and minimum transmit rate,which is modeled as a non-convex and mixed-integer fractional programming problem.Here,suboptimal resource allocation algorithms are proposed to solve this problem.First,channel assignment scheme is performed to assign channel to D2D multicast groups.Second,for a given channel assignment,iterative power allocation schemes with and without loss of cellular users’rate are completed,respectively.Simulation results corroborate the convergence performance of the proposed algorithms.In addition,compared with the traditional throughput maximization algorithm,the proposed algorithms can improve the energy efficiency of the system and the fairness achieved among different multicast groups.

Resource Allocation in D2D-Aided High-Speed Railway Wireless Communication Systems： A Matching Theory Approach

With the rapid deployment of high speed railway(HSR) worldwide,both safety operation and comfort experience can be desired to evolve into a future era of intelligent transportation system.To eliminate boredom and provide entertainment for passengers,an intranet for internal communications among passengers named as onboard social network system(SNS) is needed.In this paper,the latest progress in HSR network architectures and technology building blocks are discussed to enable the implementation of the SNS.Meanwhile,based on the device-to-device(D2 D) communication technology for proximal information interaction,SNS can be efficiently facilitated.A dynamic resource allocation algorithm is proposed to maximize the total utility of the onboard SNS,which is solved with the matching theory method.Simulation results verify the convergence and efficiency of the proposed algorithm.

Optimal Resource Allocation Method for Device-to-Device Communication in 5G Networks

With the rapid development of the next-generation mobile network,the number of terminal devices and applications is growing explosively.Therefore,how to obtain a higher data rate,wider network coverage and higher resource utilization in the limited spectrum resources has become the common research goal of scholars.Device-to-Device(D2D)communication technology and other frontier communication technologies have emerged.Device-to-Device communication technology is the technology that devices in proximity can communicate directly in cellular networks.It has become one of the key technologies of the fifth-generation mobile communications system(5G).D2D communication technology which is introduced into cellular networks can effectively improve spectrum utilization,enhance network coverage,reduce transmission delay and improve system throughput,but it would also bring complicated and various interferences due to reusing cellular resources at the same time.So resource management is one of the most challenging and importing issues to give full play to the advantages of D2D communication.Optimal resource allocation is an important factor that needs to be addressed in D2D communication.Therefore,this paper proposes an optimization method based on the game-matching concept.The main idea is to model the optimization problem of the quality-of-experience based on user fairness and solve it through game-matching theory.Simulation results show that the proposed algorithm effectively improved the resource allocation and utilization as compared with existing algorithms.

Resource Allocation and Power Control Policy for Device-to-Device Communication Using Multi-Agent Reinforcement Learning

Device-to-Device(D2D)communication is a promising technology that can reduce the burden on cellular networks while increasing network capacity.In this paper,we focus on the channel resource allocation and power control to improve the system resource utilization and network throughput.Firstly,we treat each D2D pair as an independent agent.Each agent makes decisions based on the local channel states information observed by itself.The multi-agent Reinforcement Learning(RL)algorithm is proposed for our multi-user system.We assume that the D2D pair do not possess any information on the availability and quality of the resource block to be selected,so the problem is modeled as a stochastic non-cooperative game.Hence,each agent becomes a player and they make decisions together to achieve global optimization.Thereby,the multi-agent Q-learning algorithm based on game theory is established.Secondly,in order to accelerate the convergence rate of multi-agent Q-learning,we consider a power allocation strategy based on Fuzzy C-means(FCM)algorithm.The strategy firstly groups the D2D users by FCM,and treats each group as an agent,and then performs multi-agent Q-learning algorithm to determine the power for each group of D2D users.The simulation results show that the Q-learning algorithm based on multi-agent can improve the throughput of the system.In particular,FCM can greatly speed up the convergence of the multi-agent Q-learning algorithm while improving system throughput.

A Joint Resource Allocation Algorithm for D2D Communication

The emergence of multimedia services has meant a substantial increase in the number of devices in mobile networks and driving the demand for higher data transmission rates.The result is that,cellular networks must technically evolve to support such higher rates,to be equipped with greater capacity,and to increase the spectral and energy efficiency.Compared with 4G technology,the 5G networks are being designed to transmit up to 100 times more data volume with devices whose battery life is 10 times longer.Therefore,this new generation of networks has adopted a heterogeneous and ultra-dense architecture,where different technological advances are combined such as device-to-device(D2D)communication,which is one of the key elements of 5G networks.It has immediate applications such as the distribution of traffic load(data offloading),communications for emergency services,and the extension of cellular coverage,etc.In this communication model,two devices can communicate directly if they are close to each other without using a base station or a remote access point.Thus,eliminating the interference between theD2Dand cellular communication in the network.The interference management has become a hot issue in current research.In order to address this problem,this paper proposes a joint resource allocation algorithm based on the idea of mode selection and resource assignment.Simulation results showthat the proposed algorithm effectively improves the systemperformance and reduces the interference as compared with existing algorithms.

Secure Resource Allocation in Device-to-Device Communications Underlaying Cellular Networks

With increasing the demand for transmitting secure information in wireless networks,deviceto-device(D2D)communication has great potential to improve system performance.As a well-known security risk is eavesdropping in D2D communication,ensuring information security is quite challenging.In this paper,we first obtain the closed-forms of the secrecy outage probability(SOP)and the secrecy ergodic capacity(SEC)for direct and decodeand-forward(DF)relay modes.Numerical results are presented to verify the theoretical results,and these results show the cases that the DF relay mode improves security performance compared to the direct mode at long distances between the transmitter and receiver nodes.Further,we look into the optimization problems of secure resource allocation in D2D communication to maximize the SEC and to minimize the SOP by considering the strictly positive secrecy capacity constraint as a mixed-integer non-linear programming(MINLP)problem.In the continue,we convert the MINLP to convex optimization.Finally,we solve this program with a dual method and obtain an optimal solution in the direct and DF relay modes.

Resource Allocation and Optimization in Device-to-Device Communication 5G Networks

The next-generation wireless networks are expected to provide higher capacity,system throughput with improved energy efficiency.One of the key technologies,to meet the demand for high-rate transmission,is deviceto-device(D2D)communication which allows users who are close to communicating directly instead of transiting through base stations,and D2D communication users to share the cellular user chain under the control of the cellular network.As a new generation of cellular network technology,D2D communication technology has the advantages of improving spectrum resource utilization and improving system throughput and has become one of the key technologies that have been widely concerned in the industry.However,due to the sharing of cellular network resources,D2D communication causes severe interference to existing cellular systems.One of the most important factors in D2D communication is the spectrum resources utilization and energy consumption which needs considerable attention from research scholars.To address these issues,this paper proposes an efficient algorithm based on the idea of particle swarm optimization.The main idea is to maximize the energy efficiency based on the overall link optimization of D2D user pairs by generating an allocation matrix of spectrum and power.The D2D users are enabled to reuse multiple cellular user’s resources by enhancing their total energy efficiency based on the quality of service constraints and the modification of location and speed in particle swarm.Such constraint also provides feasibility to solve the original fractional programming problem.Simulation results indicate that the proposed scheme effectively improved the energy efficiency and spectrum utilization as compared with other competing alternatives.

Resource Allocation of Agricultural Science and Technology R&D

The status quo of resource allocation of agricultural science and technology R&D(research and development)both at home and abroad,including the amount and function of agricultural science and technology research funds,human resources in the resources of agricultural science and technology R&D,the efficiency of resource allocation of agricultural science and technology R&D,the management system of agricultural scientific innovation and the operation status of scientific funds,is analyzed.The problems in the current resource allocation of agricultural science and technology R&D are put forward,including unreasonable resource allocation;low efficiency,and low efficiency of the transformation of agricultural scientific achievements.The highly effective resource allocation of agricultural science and technology R&D is analyzed from the aspects of resource allocation structure,environment,channel,spatial layout and industrial chain.

云南省R&D经费投入结构动态研究

为研究云南省“十二五”以来R&D经费投入结构变化,分析R&D经费投入现状,并与全国,东、中、西部地区及临近省份进行了对比;从资金来源和资金配置视角,运用Cobb-Douglas效用函数及其修正模型对R&D经费投入结构展开动态分析。结果表明:云南省R&D经费投入结构不尽合理,资金来源中政府资金占比较低且呈下降趋势,资金配置中基础研究和应用研究投入不足,与最优结构偏离程度较大。针对存在的问题,提出持续加大政府R&D经费投入力度、围绕产业链提升基础研究和应用研究比重的建议。

Device Discovery in D2D Communication: Scenarios and Challenges

Device to Device (D2D) communication is expected to be anessential part of 5G cellular networks. D2D communication enables closeproximitydevices to establish a direct communication session. D2D communicationoffers many advantages, such as reduced latency, high data rates,range extension, and cellular offloading. The first step to establishing a D2Dsession is device discovery;an efficient device discovery will lead to efficientD2D communication. D2D device further needs to manage its mode of communication,perform resource allocation, manage its interference and mostimportantly control its power to improve the battery life of the device. Thiswork has developed six distinct scenarios in which D2D communication canbe initiated, considering their merits, demerits, limitations, and optimizationparameters. D2D communication procedures for the considered scenarioshave been formulated, based upon the signal flow, containing device discovery,resource allocation, and session teardown. Finally, latency for each scenariohas been evaluated, based on propagation and processing delays.

我国区域R&D资源配置效率差异及其影响因素分析

运用面板数据随机前沿分析法,对我国31个省市自治区的R&D资源配置效率及其影响因素进行了分析。结果显示,我国东部沿海、东北、中、西部四大地区R&D资源配置效率呈现明显的"阶梯递减"现象;R&D资本存量要素产出弹性,远大于R&D人力资源要素产出弹性;国际贸易强度、地方政府对科技活动的支持力度等外生性因素,都对R&D资源配置效率具有显著的正向促进作用。

河北省高校R&D资源配置现状与对策建议

R&D活动是科技创新活动的核心,R&D活动的开展有赖于R&D资源的支撑,R&D资源的合理配置是推动科技发展的重要前提和保障。河北省近年来加大了对高校R&D资源的投入力度,R&D资源的利用效率得到了进一步的提高,然而目前高校R&D资源配置还存在着一些不合理之处,R&D资源配置亟待优化。首先通过横向对比,分析河北省高校、企业、研究机构的R&D资源配置的特点;接着从R&D人力投入、R&D经费投入、R&D物力投入和R&D投入的科技产出等几个方面,对河北省高校的R&D资源配置现状进行了分析总结;最后从高校主管部门的角度出发提出优化R&D资源配置,提高R&D资源利用效率的对策与建议。

我国企业与政府R&D经费投入最优比重测度

分析对近年来R&D投入来源结构的变动情况及其成因,并运用柯布-道格拉斯函数模型测度我国R&D经费的最优来源结构。研究表明,我国目前R&D经费来源结构中企业占较高比例并不意味着企业R&D投入主体地位的确立,政府R&D投入增长缓慢是导致我国R&D来源结构未实现资源优化配置的主要原因。因此,应继续加强企业的主体地位,进一步加大政府R&D投入的力度。

连云港科技资源配置效率与行业R&D有效性研究

科技资源配置效率和R&D有效性是科技管理部门在制定鼓励区域科技创新政策时必须考虑的因素。利用DEA方法对连云港科技资源配置效率和14个主要工业行业的R&D效率进行评价。研究结果表明,连云港市科技资源配置整体有效,但样本行业R&D效率表现稍差,且存在差异较大的特征。