Personal recommendation via unequal resource allocation on bipartite networks

In this paper, we present a recommendation algorithm based on the resource-allocation progresses on bipartite networks. In this model, each node is assigned an attraction that is proportional to the power of its degree, where the exponent β is an adjustable parameter that controls the configuration of attractions. In the resource-allocation process, each transmitter distributes its each neighbor a fragment of resource that is proportional to the attraction of the neighbor. Based on a benchmark database, we find that decreasing the attractions that the nodes with higher degrees are assigned can further improve the algorithmic accuracy. More significantly, numerical results show that the optimal configuration of attractions subject to accuracy can also generate more diverse and less popular recommendations.     

基于引力场理论的复杂网络路由选择策略研究

利用引力场理论对网络传输过程中节点激发的引力场进行了描述,建立了节点的引力场方程,引入α和γ两个参数,用于调节数据传输对节点畅通程度、节点传输能力和路径长度的依赖程度．基于节点的引力场,提出了一种高效的路由选择算法,该算法下数据包将沿着所受路径引力最大的方向进行传递．为检验算法的有效性,引入有序状态参数卵,利用其由自由流到拥塞态的指标流量相变值度量网络的吞吐量,并通过节点的介中心值B分析网络的传输性能和拥塞分布．针对算法在不同α,γ取值条件下的路由情况进行了仿真．仿真结果显示,与传统最短路由算法相比,本文算法将网络传输能力提高了数倍,有效地均衡了节点的介中心值分布,传输路径平均长度（Lavg）随负载量R的增加表现出先增后减的变化趋势,而参数α与γ值的变化对网络传输能力几乎没有影响,说明本文路由算法的性能不依赖于α与γ,对于可行域内任意的α与γ算法都能保证网络传输能力近似相等．     

无标度网络上队列资源分配研究

研究了节点队列资源有限的条件下,无标度网络上的信息流动力学过程,发现了网络由自由流通到拥塞的相变现象,提出了一种基于节点度的队列资源分配模型.模型的核心是使节点i的队列长度与kβi成正比(ki为节点i的度,β为分配参数).仿真结果表明,在网络使用最短路径算法进行信息包传送的条件下,β近似等于1.25时队列资源分配最合理,网络容量最大,且该最佳值与队列总资源多少以及网络的规模无关.     

Chance-constrained resource allocation for cognitive wireless-powered backscatter communication networks

With the rapid development of the Internet of Things (IoT) and the increasing number of wireless nodes, the problems of scare spectrum and energy supply of nodes have become main issues. To achieve green IoT techniques and resolve the challenge of wireless power supply, wireless-powered backscatter communication as a promising transmission paradigm has been concerned by many scholars. In wireless-powered backscatter communication networks, the passive backscatter nodes can harvest the ambient radio frequency signals for the devices’ wireless charging and also reflect some information signals to the information receiver in a low-power-consumption way. To balance the relationship between the amount of energy harvesting and the amount of information rate, resource allocation is a key technique in wireless-powered backscatter communication networks. However, most of the current resource allocation algorithms assume available perfect channel state information and limited spectrum resource, it is impractical for actual backscatter systems due to the impact of channel delays, the nonlinearity of hardware circuits and quantization errors that may increase the possibility of outage probability. To this end, we investigate a robust resource allocation problem to improve system robustness and spectrum efficiency in a cognitive wireless-powered backscatter communication network, where secondary transmitters can work at the backscattering transmission mode and the harvest-then-transmit mode by a time division multiple access manner. The total throughput of the secondary users is maximized by jointly optimizing the transmission time, the transmit power, and the reflection coefficients of secondary transmitters under the constraints on the throughput outage probability of the users. To tackle the non-convex problem, we design a robust resource allocation algorithm to obtain the optimal solution by using the proper variable substitution method and Lagrange dual theory. Simulation results verify the effectiveness of the proposed algorithm in terms of lower outage probabilities.     

Optimal dynamic bandwidth allocation for complex networks

Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.     

无标度复杂网络负载传输优化策略

提出了一种能够显著提高无标度复杂网络负载传输性能的优化路由策略.实现了负载在核心节点与边缘节点间的合理分配.分析表明该策略使得网络的负载处理能力正比于网络规模的平方,而与单个节点的度值无关.实验结果显示优化路由策略在保持了最短路由策略小世界效应的同时,成倍地提升了网络的负载传输能力,且随着网络平均节点度的增加其优势越趋显著.此外,与有效路由策略的比较进一步验证了优化路由策略的优异性能. 关键词： 优化路由策略 复杂网络 负载传输 网络阻塞     

Traffic dynamics on layered complex networks

To minimize traffic congestion, understanding how traffic dynamics depend on network structure is necessary. Many real-world complex systems can be described as multilayer structures. In this paper, we introduce the idea of layers to establish a traffic model of two-layer complex networks. By comparing different two-layer complex networks based on random and scale-free networks, we find that the physical layer is much more important to the network capacity of two-layer complex networks than the logical layer. Two-layer complex networks with a homogeneous physical topology are found to be more tolerant to congestion. Moreover, simulation results show that the heterogeneity of logical and physical topologies makes the packet-delivery process of two-layer networks more efficient in the free-flow state, without the occurrence of traffic congestion.     

复杂网络的一种加权路由策略研究

复杂网络的传输能力是其功能正常运转的重要保障,提高网络的吞吐量有着重要意义.提出一种新的高效路由策略,以提高复杂网络的传输能力,称之为加权路由策略.即对网络的每一条边加权,权值与该边的两端节点的度相关,然后数据包按照这个加权网络的最短路径路由.这样的路径可以更均匀地经过各个节点,发挥它们的传输能力,极大地提高网络的吞吐量.可以避免数据包集中地通过个别度大的节点,在这些节点发生拥塞.仿真显示,该策略比传统的最短路径策略优越,对很多结构的网络,可以提高几十倍的吞吐量. 关键词： 复杂网络 路由策略 吞吐量 拥塞     

异质化带宽分配下的复杂网络数据流负载问题研究

研究了带有连接边传输容量(带宽)约束的复杂网络上如何提升网络数据流负载问题. 在网络连接边带宽资源总量固定的条件下, 提出了一种异质化带宽分配方案. 引入 "受控边" 概念, 通过加入适当比例的 "受控边", 重新分配带宽资源, 并结合具有拥塞感知能力路由策略的数据流量模型, 利用带宽分配调节数据流量走向, 提高了带宽利用效率, 最终使得网络整体的负载能力较带宽匀质化分配时有显著提升. 分别在Barabási-Albert无标度网络和Watts-Strogtz (WS)小世界网络平台上仿真, 发现按照本文的带宽分配方案, WS小世界网络中节点连接边带宽与网络负载有较强的相关性, 节点连接边带宽分配最均衡的时候, 网络负载能力达到最大. 关键词： 异质化带宽分配 负载 介数 受控边     

基于博弈论的认知无线电网络跨层资源分配

针对多跳认知无线电网络的多层资源分配问题,提出了协作去耦合方法和跨层联合方法,协作去耦合方法首先单独完成路径选择任务,随后进行信道与功率的博弈分配;跨层联合方法则通过博弈直接对路径、信道、功率三层资源进行同时分配,两种方法都综合考虑网络层、介质访问控制层、物理层的启发原则,引入了节点被干扰度信息和节点主动干扰度信息来辅助路径选择,设计了基于功率允许宽度信息的Boltzmann探索来完成信道与功率选择,设计了长链路和瓶颈链路替换消除机制以进一步提高网络性能,从促进收敛角度,选择序贯博弈并设计了具体的博弈过程,此外还分析了博弈的纳什均衡,讨论了两种算法的复杂度,仿真结果表明,协作去耦合方法和跨层联合方法在成功流数量、流可达速率、发射功耗性能指标上均优于简单去耦合的链路博弈、流博弈方法。     

A self-organizing shortest path finding strategy on complex networks

The shortcomings of traditional methods to find the shortest path are revealed, and a strategy of finding the self-organizing shortest path based on thermal flux diffusion on complex networks is presented. In our method, the shortest paths between the source node and the other nodes are found to be self-organized by comparing node temperatures. The computation complexity of the method scales linearly with the number of edges on underlying networks. The effects of the method on several networks, including a regular network proposed by Ravasz and Barabási which is called the RB network, a real network, a random network proposed by Ravasz and Barabási which is called the ER network and a scale-free network, are also demonstrated. Analytic and simulation results show that the method has a higher accuracy and lower computational complexity than the conventional methods.     

一种复杂网络路由策略的普适优化算法

现有的复杂网络路由策略很多,改进算法也不断涌现,但是目前还没有一个统一的标准来衡量算法是否达到网络最佳传输效果.针对这一问题,本文提出一种适用于现有路由策略的普适优化算法.首先通过理论分析指出制约网络传输能力的关键因素是最大介数中心度,因而"最大介数中心度是否已经最低"成为评判路由策略是否最优的标准.在此基础上,采用"惩罚选择法"避开网络中介数中心度值比较大的节点,使网络介数中心度值分布更均匀,均衡网络中各个节点的传输负载.仿真结果显示,该优化算法针对现有路由策略均能降低最大介数中心度值,大幅度提高网络的传输能力.     

混沌免疫算法求解认知无线电网络资源分配问题

为了优化认知无线电网络中多用户正交频分复用子载波的资源分配, 将其转换为一个约束优化问题, 进而提出了一种基于混沌免疫优化的求解方法.给出了算法的实现过程和关键技术, 设计了适合算法求解的编码、 克隆、 重组、 变异算子.实验结果表明, 在满足认知用户速率、 所需误码率及干扰约束的条件下, 本文所用算法减小了整个系统所需的总发射功率, 同时收敛速度较快, 能够得到较优的子载波分配方案, 进而提高频谱利用效率.     

一类基于随机行走机理的优化路由改进策略

在对随机行走过程的研究中发现:单个粒子通过某条特定路径的时间正比于该路径上所有节点度的连乘积.据此,文章提出基于随机行走机理的优化路由改进策略.该策略以节点度连乘积最小化为原则,通过调节可变参数,建立节点处理能力均匀分布的情况下最佳路由策略.通过分析比较不同路由策略条件下平均路由介数中心度,网络的临界负载量,平均路径长度以及平均搜索信息量等性能指标,研究结果表明,此改进路由策略在保证网络平均路径长度较少增加的前提下,使网络的传输能力获得最大幅度的提升. 关键词： 复杂网络 路由策略 负载传输     

Game theory-based radio resource allocation in NOMA vehicular communication networks supported by UAV

Vehicular communication networks are emerging as a promising technology to provide high-quality internet service such as entertainment for road users via infrastructure-to-vehicle (I2V) communication, and to guarantee road users’ safety via vehicle-to-vehicle (V2V) communication. Some technical issues that impact the performance of these networks are the lack of or poor communication paths between vehicles, and the limitation of radio resources. Unmanned aerial vehicles (UAVs) as promising solutions for supporting vehicular networks could provide communication coverage in hazardous environments and areas with no capacities for installation or maintenance of ground base stations (BSs). Also, non-orthogonal multiple access (NOMA) methods can improve spectral and energy efficiency and thereby allow more users to be connected to the desired network. In this paper, exploring the NOMA, we develop a scheme for optimum resource allocation in presence of a UAV that supports vehicular communications. Resource allocation for this scenario is formulated as a mixed-integer non-linear programming (MINLP) problem. Due to the high complexity of such problems, we propose two low-complexity near-optimal methods. First, we apply difference-of-concave-functions (DC) approximations to solve the problem in an iterative process. Next, we use Stackelberg game-based method for efficient solving, and then, closed-form expressions of optimal power allocations using KKT-conditions are derived. Simulations illustrate the effectiveness of the proposed scheme along with the Stackelberg game-based method.     

Optimized routing strategy for complex network with multiple priorities

Different loads in the network require distinct Qo S standard, while present routing strategies for complex networks ignored this fact. To solve this problem, we designed a routing strategy RS-MP with multiple priorities by which packets are classified into privileged-packets and common-packets. In RS-MP, privileged-packets route by the Shortest Path Algorithm, and do not need to queue up. Common-packets' routes are determined by a new factor BJmax of the network. The BJmax stands for the largest betweenness centrality. By minimizing BJmax, the throughout capacity of the network can be maximized. The simulation results show that RS-MP can guarantee privileged-packets with the shortest path length and smallest delay, and maximized throughout capacity for common packets in the no-congestion state.     

Probability routing strategy for scale-free networks

This study proposes a probability routing strategy for improving traffic capability on scale-free networks. Compared with the shortest path routing strategy depending on central nodes largely and the efficient routing strategy avoiding hub routers as much as possible, the probability routing strategy makes use of hub routers more efficiently, transferring approximate average amount of packs of the whole network. Simulation results indicate that the probability routing strategy has the highest network capacity among the three routing strategies. This strategy provides network capacity that can be more than 30 times higher than that of the shortest path routing strategy and over 50% higher than that of the efficient routing strategy. In addition, the average routing path length of our proposed strategy is over 10% shorter than that of the efficient routing strategy and only about 10% longer than that of the shortest path routing strategy.     

Effective usage of shortest paths promotes transportation efficiency on scale-free networks

With rapid economic and social development, the problem of traffic congestion is getting more and more serious. Accordingly, network traffic models have attracted extensive attention. In this paper, we introduce a shortest-remaining-path-first queuing strategy into a network traffic model on Barabási–Albert scale-free networks under efficient routing protocol, where one packet’s delivery priority is related to its current distance to the destination. Compared with the traditional first-in-first-out queuing strategy, although the network capacity has no evident changes, some other indexes reflecting transportation efficiency are significantly improved in the congestion state. Extensive simulation results and discussions are carried out to explain the phenomena. Our work may be helpful for the designing of optimal networked-traffic systems.     

A multi-timescale resource allocation algorithm based on self-learning for distributed fog radio access networks

Faced with limited network resources, diverse service requirements and complex network structures, how to efficiently allocate resources and improve network performances is an important issue that needs to be addressed in 5G or future 6G networks. In this paper, we propose a multi-timescale collaboration resource allocation algorithm for distributed fog radio access networks (F-RANs) based on self-learning. This algorithm uses a distributed computing architecture for parallel optimization and each optimization model includes large time-scale resource allocation and small time-scale resource scheduling. First, we establish a large time-scale resource allocation model based on long-term average information such as historical bandwidth requirements for each network slice in F-RAN by long short-term memory network (LSTM) to obtain its next period required bandwidth. Then, based on the allocated bandwidth, we establish a resource scheduling model based on short-term instantaneous information such as channel gain by reinforcement learning (RL) which can interact with the environment to realize adaptive resource scheduling. And the cumulative effects of small time-scale resource scheduling will trigger another round large time-scale resource reallocation. Thus, they constitute a self-learning resource allocation closed loop optimization. Simulation results show that compared with other algorithms, the proposed algorithm can significantly improve resource utilization.     

Traffic of indistinguishable particles in complex networks

In this paper, we apply a simple walk mechanism to the study of the traffic of many indistinguishable particles in complex networks. The network with particles stands for a particle system, and every vertex in the network stands for a quantum state with the corresponding energy determined by the vertex degree. Although the particles are indistinguishable, the quantum states can be distinguished. When the many indistinguishable particles walk randomly in the system for a long enough time and the system reaches dynamic equilibrium, we find that under different restrictive conditions the particle distributions satisfy different forms, including the Bose--Einstein distribution, the Fermi--Dirac distribution and the non-Fermi distribution (as we temporarily call it). As for the Bose--Einstein distribution, we find that only if the particle density is larger than zero, with increasing particle density, do more and more particles condense in the lowest energy level. While the particle density is very low, the particle distribution transforms from the quantum statistical form to the classically statistical form, i.e., transforms from the Bose distribution or the Fermi distribution to the Boltzmann distribution. The numerical results fit well with the analytical predictions.