基于改进RAN算法的调制分类器

资源分配网络(RAN)算法可以在线训练径向基(RBF)神经网络,但算法中的网络隐层中心通常从零开始,容易受噪声和异常数据影响。文章提出一种改进RAN算法,将遗传算法(GA)对数据优化聚类后的中心点作为RAN算法的初始中心。仿真表明,基于改进算法设计的RBF调制分类器,结构简洁,识别准确率高。

永磁同步电机神经网络速度控制器设计

针对永磁同步电机非线性、多参数变化以及系统扰动等问题,将传统PID控制与具有强自适应、自学习能力的径向基函数(RBF)神经网络相结合,设计一种永磁同步电机神经网络速度控制器。用RBF神经网络自适应整定PID速度控制器参数,提高系统鲁棒性和控制精度;利用改进资源分配网络(IRAN)和梯度下降法进行离线学习和在线学习,提高RBF神经网络的运算速度。通过MATLAB仿真实验,相比于传统PID速度控制,神经网络速度控制器具有更高的控制精度,更好的调速性能和鲁棒性。

机场新增卫星厅对登机口影响的评估方法

为了解决航站楼客流量饱和的问题,采用一种增加卫星厅的方法,实现了旅客分流.基于单目标整数线性规划和多目标优化的方法,分别构建了登机口优化分配网络模型和多目标优化模型.利用登机口优化分配网络算法筛选出所使用的共同登机口,建立了目标函数并列出约束条件,采用目标约束法对建立的模型进行求解.在此基础上,根据目标建模的思想,建立了可供中转旅客总体流程时间最短且使用登机口数量最小的航班-登机口分配模型.利用MATLAB计算可知,利用42个登机口即可实现303架航班的正常运转.

Lazy loading algorithm for traffic assignment of road networks under fixed charge condition

The measures of path charge are important considerations in traffic assignment of road networks. Factors, such as travel time, fixed charge and traffic congestion which affect road users＇ choices of trip paths, are analyzed. Travelers usually decide their trip paths based on their personal habits, preferences and the information at hand. By considering both deterministic and stochastic factors which affect the value of time （VOT） during the process of path choosing, a variational inequality model is proposed to describe the problem of traffic assignment. A lazy loading algorithm for traffic assignment is designed to solve the proposed model, and the calculation steps are given. Numerical experiment results show that compared with the all-or-nothing assignment, the proposed model and the algorithm can provide more optimal traffic assignments for road networks. The results of this study can be used to optimize traffic planning and management.

A Fairness Resource Allocation Algorithm for Coverage and Capacity Optimization in Wireless Self-Organized Network

To achieve the higher resource efficiency, Coverage and Capacity Optimization(CCO) as an important role of the network self-healing and self-optimization, has become a focus topic in wireless Self-Organized Network(SON). In this paper, a novel CCO scheme is proposed to maximize utility function of the integrated coverage and capacity. It starts with the analysis on the throughput proportional fairness(PF) algorithm and then proposes the novel Coverage and Capacity Proportional Fairness(CCPF) allocation algorithm along with a proof of the algorithms convergence. This proposed algorithm is applied in a coverage capacity optimization scheme which can guarantee the reasonable network capacity by the coverage range accommodation. Next, we simulate the proposed CCO scheme based on telecom operators' real network data and compare with three typical resource allocation algorithms: round robin(RR), proportional fairness(PF) and max C/I. In comparison of the PF algorithm, the numerical results show that our algorithm increases the average throughput by 1.54 and 1.96 times with constructed theoretical data and derived real network data respectively.

A New Power Allocation Algorithm in Cognitive Radio Networks

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.

Global Optimization for the Synthesis of Integrated Water Systems with Particle Swarm Optimization Algorithm

The problem of optimal synthesis of an integrated water system is addressed in this study, where water using processes and water treatment operations are combined into a single network such that the total cost of fresh water and wastewater treatment is globally minimized. A superstructure that incorporates all feasible design alterna- tives for wastewater treatment, reuse and recycle, is synthesized with a non-linear programming model. An evolutionary approach--an improved particle swarm optimization is proposed for optimizing such systems. Two simple examples are .Presented.to illustrate the global op.timization of inte.grated water networks using the proposed algorithm.

A Robust Energy Efficiency Power AllocationAlgorithm in Cognitive Radio Networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks(CRNs),which may lead to performance degradation in real systems with disturbances or uncertainties,we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio(CR)systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint.The ellipsoid sets are used to describe the channel uncertainty,and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach.A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented.Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio(SINR)requirement of each cognitive user under channel uncertainty.

Joint Resource Allocation Using Evolutionary Algorithms in Heterogeneous Mobile Cloud Computing Networks

The problem of joint radio and cloud resources allocation is studied for heterogeneous mobile cloud computing networks. The objective of the proposed joint resource allocation schemes is to maximize the total utility of users as well as satisfy the required quality of service(QoS) such as the end-to-end response latency experienced by each user. We formulate the problem of joint resource allocation as a combinatorial optimization problem. Three evolutionary approaches are considered to solve the problem: genetic algorithm(GA), ant colony optimization with genetic algorithm(ACO-GA), and quantum genetic algorithm(QGA). To decrease the time complexity, we propose a mapping process between the resource allocation matrix and the chromosome of GA, ACO-GA, and QGA, search the available radio and cloud resource pairs based on the resource availability matrixes for ACOGA, and encode the difference value between the allocated resources and the minimum resource requirement for QGA. Extensive simulation results show that our proposed methods greatly outperform the existing algorithms in terms of running time, the accuracy of final results, the total utility, resource utilization and the end-to-end response latency guaranteeing.

Mode Selection and Resource Allocation for Deviceto-Device Communications in 5G Cellular Networks

To meet the increasing demand of wireless broadband applications in future 5G cellular networks, Device-to-Device(D2D) communications serve as a candidate paradigm to improve spectrum efficiency. Considering the challenges after D2 D transmission is introduced for future cellular networks, this paper deals with mode selection and resource allocation issues related with D2 D communications. First, we propose a mode selection scheme which aims at guaranteeing the transmission of cellular users and also considering the potential interference. We analyze the condition under which D2 D underlay mode should be used. Second, we answer the question of "how to effectively reuse cellular resource once underlaying mode is adopted". We further present a resource allocation scheme that focuses on minimizing overall interference as well as a power control method to improve the performance of D2 D systems. Simulation results demonstrate that system parameters greatly affect the switching condition of mode selection and probability of choosing underlay mode. Furthermore, for D2 D underlaying scenario, the proposed resource allocation algorithm guarantees the transmission of cellular users with consideration of transmission requirements of D2 D users. Hence, the proposed scheme can achieve better user experience.

An Ant Colony Algorithm Based on Cross-Layer Design for Routing and Wavelength Assignment in Optical Satellite Networks

This paper introduces an ant colony routing and wavelength assignment algorithm based on cross-layer design(CL-ACRWA),which can overcome the adverse effects of Doppler wavelength shift on data transmission in optical satellite networks. Firstly, a cross-layer optimization model is built, which considers the Doppler wavelength shift, the transmission delay as well as wavelength-continuity constraint. Then an ant colony algorithm is utilized to solve the cross-layer optimization model, resulting in finding an optimal light path satisfying the above constraints for every connection request. The performance of CL-ACRWA is measured by the communication success probability, the convergence property and the transmission delay. Simulation results show that CL-ACRWA performs well in communication success probability and has good global search ability as well as fast convergence speed. Meanwhile, the transmission delay can meet the basic requirement of real-time transmission of business.

An Advanced Spectrum Allocation Algorithm for The Across-Cell D2D Communication in LTE Network with Higher Throughput

In the hybrid LTE cellular network with D2D(Device-to-Device) communication, D2D communication technologies can improve the spectral efficiency significantly. However, the D2D users have to reutilize the spectrum which is allocated to the cellular users. Therefore, the co-channel interference will be more complicated in the case of crosscell D2D communications. In this article, a novel spectrum allocation algorithm for inter-cell D2D communication considering the traffic load is proposed. The traffic load can be balanced by the proposed algorithm. Meanwhile D2D users can multiplex the spectrum allocated to a number of cellular users with a certain percentage to meet the requirements of Qo S of D2D communications and reduce the interference to cellular users. Finally, the simulation results demonstrate that the proposed algorithm can meet the needs of D2D users, balance the traffic load and improve the overall throughput of the system.

Joint Link Allocation and Rate Assignment Algorithm for Multi-Channel Wireless Networks

This paper presents a link allocation and rate assignment algorithm for multi-channel wireless networks. The objective is to reduce network con-flicts and guarantee the fairness among links. We first design a new network model. With this net-work model, the multi-channel wireless network is divided into several subnets according to the num-ber of channels. Based on this, we present a link allocation algorithm with time complexity O（l^2）to al-locate all links to subnets. This link allocation algo-rithm adopts conflict matrix to minimize the network contention factor. After all links are allocated to subnets, the rate assignment algorithm to maximize a fairness utility in each subnet is presented. The rate assignment algorithm adopts a near-optirml al-gorithm based on dual decomposition and realizes in a distributed way. Simulation results demonstrate that, compared with IEEE 802.11b and slotted see-ded channel hopping algorithm, our algorithm de-creases network conflicts and improves the net-work throughput significantly.

Stereo garage parking space allocation model and simulation analysis

Based on grey neural network and particle swarm optimization algorithm,an automated stereo garage decision model is proposed to solve the problems of long waiting queue and low efficiency of automated parking garage.The gray neural network is used to forecast the stay time of the vehicle and particle swarm optimization algorithm is used to allocate the parking spaces in the stereo garage.The proposed stereo garage mathematical model is established on condition that vehicle arrival interval obeys Poisson distribution.The performance of stereo garage is evaluated by the average waiting time,average waiting queue length,average service time and average energy consumption of the customers.By comparing the efficiency indexes of the existing model based on near-distribution principle and the proposed model based on gray neural network and particle swarm algorithm,it is proved that the proposed model based on gray neural network and particle swarm algorithm is effective in improving the efficiency of garage operation and reducing the energy consumption of garage.

Dynamic Subcarrier Assignment for OFDM-PON Network Based on RSOA for ACCORDANCE

In this paper, we overview the principle of Orthogonal Frequency Division Multiplexing Passive Optical Network （OFDM-PON） systems, with a particular focus on upstream architectures capable of achieving 10Gbit/s colorless upstream transmission using Reflective Semiconductor Optical Amplifier （RSOA）. We propose an architecture of RSOA based OFDM-PON which can achieve 10Gbit/s upstream transmission over a single wavelength. A novel Dynamic Subcarrier Assignment （DSA） algorithm is also proposed to support my architecture, namely Service based Polling in Pipeline （SPP） dynamic subcarrier algorithm. A simulation was conducted to study the performance of SPP algorithm. Compared with the traditional dynamic bandwidth allocation algorithms, service based polling meets the quality of in pipeline algorithm service requirements excellently, and adapts orthogonal frequency division multiplexing passive optical network better with higher bandwidth efficiency and lower algorithm complexity.

Survivable Traffic Cognition Algorithm with Joint Failure Probability in Flexible Bandwidth Optical Networks

This paper addresses the problem of survivable traffic assignment with failure probability requirement in flexible bandwidth optical networks. We describe a Survivable Traffic Cognition (STC) algorithm with joint failure probability. Survivable Traffic Assignment (STA) algorithm and Conventional Traffic Assignment (CTA) algorithm are added to illustrate the effectiveness of our proposed STC. We investigate the effect of joint failure probability on blocking probability, spectral utilization ratio, average joint failure probability, and the average hops. Simulation results show that our proposed STC not only achieves better performance in terms of blocking probability and spectral utilization ratio than CTA and STA, but also does not cause higher average joint failure probability or larger average hops compared with STA. As a result, STC makes the best use of spectral resources and does not cause large average joint failure probability.

Noisy Chaotic Neural Network for Resource Allocation in High-Speed Train OFDMA System

High-speed train communication system is a typical high-mobility wireless communication network. Resource allocation problem has a great impact on the system performance. However, conventional resource allocation approaches in cellular network cannot be directly applied to this kind of special communication environment. A multidomain resource allocation strategy was proposed in the orthogonal frequency-division multiple access(OFDMA) of high-speed. By analyzing the effect of Doppler shift, sub-channels, antennas, time slots and power were jointly considered to maximize the energy efficiency under the constraint of total transmission power. For the purpose of reducing the computational complexity, noisy chaotic neural network algorithm was used to solve the above optimization problem. Simulation results showed that the proposed resource allocation method had a better performance than the traditional strategy.

APO-Based Parallel Algorithm of Channel Allocation for Cognitive Networks

This article investigates channel allocation for cognitive networks, which is difficult to obtain the optimal allocation distribution. We first study interferences between nodes in cognitive networks and establish the channel allocation model with interference constraints. Then we focus on the use of evolutionary algorithms to solve the optimal allocation distribution. We further consider that the search time can be reduced by means of parallel computing, and then a parallel algorithm based APO is proposed. In contrast with the existing algorithms, we decompose the allocation vector into a number of sub-vectors and search for optimal allocation distribution of sub-vector in parallel. In order to speed up converged rate and improve converged value, some typical operations of evolutionary algorithms are modified by two novel operators. Finally, simulation results show that the proposed algorithm drastically outperform other optimal solutions in term of the network utilization.

Resource allocation for relay assisted cognitive radio network

Different schemes, which performed channel, power and time allocation to enhance the network performance of overall end-to-end throughput for cooperative cognitive radio network, were investigated. Interference temperature limit of corresponding primary users was considered. Due to the constraints caused by multiple dual channels, the power allocation problem is non-convex and NP-hard. Based on geometric programming (GP), a novel and general algorithm, which turned the problem into a series of GP problems by logarithm approximation (LASGP), was proposed to efficiently solve it. Numerical results verify the efficiency and availability of the LASGP algorithm. Solutions of LASGP are provably convergent and globally optimal point can be observed as well as the channel allocation always outperforms power or timeslot allocation from simulations. Compared with schemes without any allocation, the scheme with joint channel, power and timeslot allocation significantly increases the overall end-to-end throughput by no less than 70% under same simulation conditions. This scheme can not only maximize the throughput by increasing total maximum power of relay node, but also outperform other resource allocation schemes when lower total maximum power of source and relay nodes is restricted. As the total maximum power of source node increases, the scheme with joint channel and timeslot allocation performs best in all schemes.

A Novel Resource Allocation Algorithm based on Downlink and Uplink Decouple Access Scheme in Heterogeneous Networks

Traditional cellular network requires that a user equipment(UE) should associate to the same base station(BS) in both the downlink(DL) and the uplink(UL). Based on dual connectivity(DC) introduced in LTE-Advanced R12, DL/UL decouple access scheme has been proposed, which is especially suitable for heterogeneous networks(Het Nets). This paper is the pioneer to take the DL/UL decouple access scheme into consideration and develop a novel resource allocation algorithm in a two-tier Het Net to improve the total system throughput in the UL and ease the load imbalance between macro base stations(MBSs) and pico base stations(PBSs). A model is formulated as a nonlinear integer programming, and the proposed algorithm is a sub-optimal algorithm based on the graph theory. First, an undirected and weighted interference graph is obtained. Next, the users are grouped to let users with large mutual interferences to be assigned to different clusters. Then, the users in different clusters are allocated to different resource blocks(RBs) by using the Hungarian algorithm. Simulation results show that the proposed algorithm can provide great promotions for both the total system throughput and the average cell edge user throughput and successfully ease the load imbalance between MBSs and PBSs.