Adaptive joint bandwidth and power allocation in heterogeneous wireless access environment

Joint bandwidth and power allocation for a multi-radio access(MRA)system in a heterogeneous wireless access environment is studied.Since both the number of users being served by the system and the wireless channel state are time-varying,the optimal resource allocation is no longer a static optimum and will change with the varying network state.Moreover,distributed resource allocation algorithms that require iterative updating and signaling interactions cannot converge in negligible time.Thus,it is unrealistic to assume that the active user number and the wireless channel state remain unchanged during the iterations.In this paper,we propose an adaptive joint bandwidth and power allocation algorithm based on a novel iteration stepsize selection method,which can adapt to the varying network state and accelerate the convergence rate.A distributed solution is also designed for the adaptive joint resource allocation implementation.Numerical results show that the proposed algorithm can not only track the varying optimal resource allocation result much more quickly than a traditional algorithm with fixed iteration stepsize,but can also reduce the data transmission time for users and increase the system throughput.     

Access point selection in heterogeneous wireless networks using belief propagation

The next generation wireless network will be composed by various heterogenous wireless access networks,such as cellular network,worldwide interoperability for microwave access(WiMAX),wireless local area network(WLAN),etc.Different access networks cooperatively provide high-bandwidth connectivity with bandwidth guarantees.This paper proposes a utility-based access point selection scheme,which selects an accessible point for each user,such that the bandwidth requirement of each user is satisfied,and also the defined utility function is maximized.Due to the NP-complete nature of the problem,the existing proposals apply the greedy method to find a solution.We find that belief propagation is an efficient tool to solve this problem,and thus,we derive the same optimization objective in a new way,and then draw a factor graph representation which describes our combinatorial optimization problem.Afterwards,we develop the belief propagation algorithm,and show that our algorithm converges.Finally,we conduct numerical experiments to evaluate the convergency and accuracy of the belief propagation in load balancing problem.     

Utility-based bandwidth allocation algorithm for heterogeneous wireless networks

In next generation wireless network (NGWN), mobile users are capable of connecting to the core network through various heterogeneous wireless access networks, such as cellular network, wireless metropolitan area network (WMAN), wireless local area network (WLAN), and ad hoc network. NGWN is expected to provide high-bandwidth connectivity with guaranteed quality-of-service to mobile users in a seamless manner; however, this desired function demands seamless coordination of the heterogeneous radio access network (RAN) technologies. In recent years, some researches have been conducted to design radio resource management (RRM) architectures and algorithms for NGWN; however, few studies stress the problem of joint network performance optimization, which is an essential goal for a cooperative service providing scenario. Furthermore, while some authors consider the competition among the service providers, the QoS requirements of users and the resource competition within access networks are not fully considered. In this paper, we present an interworking integrated network architecture, which is responsible for monitoring the status information of different radio access technologies (RATs) and executing the resource allocation algorithm. Within this architecture, the problem of joint bandwidth allocation for heterogeneous integrated networks is formulated based on utility function theory and bankruptcy game theory. The proposed bandwidth allocation scheme comprises two successive stages, i.e., service bandwidth allocation and user bandwidth allocation. At the service bandwidth allocation stage, the optimal amount of bandwidth for different types of services in each network is allocated based on the criterion of joint utility maximization. At the user bandwidth allocation stage, the service bandwidth in each network is optimally allocated among users in the network according to bankruptcy game theory. Numerical results demonstrate the efficiency of 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 pro- posed 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）.     

Performance analysis of three multi-radio access control policies in heterogeneous wireless networks

Access control policy in wireless networks has a significant impact on QoS satisfaction and resource utilization efficiency. The design of access control policy in heterogeneous wireless networks （HWNs） becomes more challenging especially for the heterogeneous multiple access protocols of each radio network. In this paper, a Markov model is proposed to analyze the performance of three access control policies for HWNs. The first policy is the optimal radio access technology （O-RAT） selection, where the incoming traffic always tries to access one network with the maximum service rate before admission. The second policy intends to allocate the same data to all networks. And the traffic will leave the system if it is accomplished first by one of these networks, which is formulated as the aggregated multi-radio access （A-MRA） technology. The third policy is named the parallel multi-radio access （P-MRA） transmission, in which the incoming traffic is split into different networks. The traffic is served with the sum of the service rates provided by overall networks. Numerical and simulate results show the effectiveness of our analytical framework and the performance gain of the three access control policies. As illustrated with some representative results, the P-MRA policy shows superior performance gain to the other two policies independent on the specific parameters of the different multiple access protocols due to the multiplexing gain.     

Dynamic emulation based modeling and detection of polymorphic shellcode at the network level

It is a promising way to detect polymorphic shellcode using emulation method. However, previous emulation-based approaches are limited in their performance and resilience against evasions. A new enhanced emulation-based detection approach is proposed, including an automaton-based model of the dynamic behavior of polymorphic shellcode and a detection algorithm, the detection criterion of which is derived from that model and ensures high detection accuracy. The algorithm also contains several optimization techniques, highly improving the running performance and the resilience against detection evasion shellcode. We have implemented a prototype system for our approach. The advantages of our algorithm are validated by the experiments with real network data, polymorphic shellcode samples generated by available polymorphic engines and hand-crafted detection evasion shellcode.     

Performance Modeling and Evaluation for Optical Access Networks

GEPON （gigabit Ethemet passive optical network） is widely deployed to realize the economical FTTH （fiber-to-the home） system for supporting high speed Internet and video distribution service. Looking ahead, next home FTTH standardization activities, 10G-EPON, were completed at the IEEE 802.3av Task Force in 2009, and the standards had specified the co-existence of 10G-EPON on the same exist optical access distribution network as a cost-effective solution. A key device in such a system is an optical receiver IC （integrated circuit） with a quick response and high sensitivity that realizes high efficiency in data transmission. This paper also reports burst-mode optical receiver ICs fabricated using 0.25 ktm SiGe BiCMOS （Silicon-germanium Bipolar Complementary Metal Oxide Semiconductor） technologies for a 10G-EPON system, which is a promising access network for a next generation PON system. The results show that SiGe BiCMOS can provide high performance and cost effective receiver ICs for 10G-class PON systems. The authors use PEPA （performance evaluation process algebra） to evaluate a typical PON system＇s performance. The approach is more convenient, flexible and lower cost than the former simulation method which needs to develop special hardware and software tools. Moreover, they can easily analyze how changes in performance depend on changes in a particular mode by supplying ranges for parameter values.     

A Dynamic Load Balancing Mechanism for Distributed Systems

It is desirable in a distributed system to have the system load balanced evenly among the nodes so that the mean job response time is minimized.In this paper,we present a dynamic load balancing mechanism(DLB).It adopts a cntralized approach and is network topology independent.The DLB mechanism employs a set of threscholds which are automatically adjusted as the system load changes.It also provides a simple mechanism for the system to switch between periodic and instantaneous load balancing policies with ease.The performance of the proposed algorithm is evaluated by intensive simulations for various parameters.Te simulation results show that the mean job response time in a system implementing DLB algorithm is significantly lower than the same system without load balancings.Furthermore,compared with a previously proposed algorithm,DLB algorithm demonstrates improved performance,especially when the system is heavily loaded and the load is unevenly distributed.     

Speed up Training of the Recurrent Neural Network Based on Constrained optimization Techniques

In this paper,the constrained optimization technique for a substantial problem is explored,that is accelerating training the globally recurrent neural network.Unlike most of the previous methods in feedforware neural networks,the authors adopt the constrained optimization technique to improve the gradientbased algorithm of the globally recurrent neural network for the adaptive learning rate during tracining.Using the recurrent network with the improved algorithm,some experiments in two real-world problems,namely,filtering additive noises in acoustic data and classification of temporat signals for speaker identification,have been performed.The experimental results show that the recurrent neural network with the improved learning algorithm yields significantly faster training and achieves the satisfactory performance.     

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle (HEV) that operates with adaptive cruise control (ACC). Real-time energy optimization is an essential issue such that the HEV powertrain system is as efficient as possible. With connected vehicle technique, ACC system shows considerable potential of high energy efficiency. Combining a classical ACC algorithm, a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon. The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle. The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.     

Direct heuristic dynamic programming based on an improved PID neural network

In this paper,an improved PID-neural network(IPIDNN) structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming(DHDP).As one of online learning algorithm of approximate dynamic programming(ADP),DHDP has demonstrated its applicability to large state and control problems.Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately,it is not always possible to access all the states in a real system.This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control.Since this structure can estimate the integrals and derivatives of measurable outputs,more system states are utilized and thus better control performance are expected.Compared with traditional PIDNN,this configuration is flexible and easy to expand. Based on this structure,a gradient decent algorithm for this IPIDNN-based DHDP is presented.Convergence issues are addressed within a single learning time step and for the entire learning process.Some important insights are provided to guide the implementation of the algorithm.The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.     

A novel initial ranging algorithm for OFDMA systems using smart antenna

To improve the capacity of initial ranging （IR） users simultaneously accessing into network and enhance the performance of the IR, a novel IR algorithm is presented for the orthogonal frequency division multiple access systems with the smart antenna. The beamforming weight vectors provided by the antenna array are introduced for the IR users, and the procedures of the multiuser parameter estimation and the interference cancellation are designed. The proposed method can improve the accuracy of the channel estimation of active paths and efficiently reduce the residual multiple access interference. Simulation results show that the proposed method has a much better performance than the available successive multiuser detection and interference cancellation algorithm and accommodates more active ranging users simultaneously accessing into a cell.     

动态交通分配与信号控制的组合模型及算法研究

This paper presents a generalized bi-level programming model of combined dynamic traffic assignment and traffic signal control, and especially analyzes a procedure for determining the equilibrium queuing delays on saturated links for dynamic network signal control satisfying the FIFO (first-in-first-out) rule. The chaotic optimal algorithm proposed in this paper can not only present the optimal signal settings, but also calculate, at each interval, the link inflow rates and outflow rates for the dynamic user optimal problem, and provide real-time information for the travelers. Finally, a numerical example is given to illustrate the application of the proposed model and solution algorithm, and comparison shows that this model has better system performance.     

Acceleration Factor Harmonious Particle Swarm Optimizer

A Particle Swarm Optimizer (PSO) exhibits good performance for optimization problems, although it cannot guarantee convergence to a global, or even local minimum. However, there are some adjustable parameters, and restrictive conditions, which can affect the performance of the algorithm. In this paper, the sufficient conditions for the asymptotic stability of an acceleration factor and inertia weight are deduced, the value of the inertia weight ω is enhanced to (-1,1). Furthermore a new adaptive PSO algorithm - Acceleration Factor Harmonious PSO (AFHPSO) is proposed, and is proved to be a global search algorithm. AFHPSO is used for the parameter design of a fuzzy controller for a linear motor driving servo system. The performance of the nonlinear model for the servo system demonstrates the effectiveness of the optimized fuzzy controller and AFHPSO.     

Stochastic computer network with multiple terminals under total accuracy rate

From the viewpoint of service level agreements,data transmission accuracy is one of the critical performances for assessing Internet by service providers and enterprise customers.The stochastic computer network(SCN),in which each edge has several capacities and the accuracy rate,has multiple terminals.This paper is aimed mainly to evaluate the system reliability for an SCN,where system reliability is the probability that the demand can be fulfilled under the total accuracy rate.A minimal capacity vector allows the system to transmit demand to each terminal under the total accuracy rate.This study proposes an efficient algorithm to find all minimal capacity vectors by minimal paths.The system reliability can then be computed in terms of all minimal capacity vectors by the recursive sum of disjoint products(RSDP) algorithm.     

Design of robotic visual servo control based on neural network and genetic algorithm

A new visual servo control scheme for a robotic manipulator is presented in this paper, where a back propagation (BP) neural network is used to make a direct transition from image feature to joint angles without requiring robot kinematics and camera calibration. To speed up the convergence and avoid local minimum of the neural network, this paper uses a genetic algorithm to find the optimal initial weights and thresholds and then uses the BP algorithm to train the neural network according to the data given. The proposed method can effectively combine the good global searching ability of genetic algorithms with the accurate local searching feature of BP neural network. The Simulink model for PUMA560 robot visual servo system based on the improved BP neural network is built with the Robotics Toolbox of Matlab. The simulation results indicate that the proposed method can accelerate convergence of the image errors and provide a simple and effective way of robot control.     

Spray and forward:Efficient routing based on the Markov location prediction model for DTNs

Typical delay tolerant networks(DTNs)often suffer from long and variable delays,frequent connectivity disruptions,and high bit error rates.In DTNs,the design of an efficient routing algorithm is one of the key issues.The existing methods improve the accessibility probability of the data transmission by transmitting many copies of the packet to the network,but they may cause a high network overhead.To address the tradeoff between a successful delivery ratio and the network overhead,we propose a DTN routing algorithm based on the Markov location prediction model,called the spray and forward routing algorithm(SFR).Based on historical information of the nodes,the algorithm uses the second-order Markov forecasting mechanism to predict the location of the destination node,and then forwards the data by greedy routing,which reduces the copies of packets by spraying the packets in a particular direction.In contrast to a fixed mode where a successful-delivery ratio and routing overhead are contradictory,a hybrid strategy with multi-copy forwarding is able to reduce the copies of the packets efficiently and at the same time maintain an acceptable successful-delivery ratio.The simulation results show that the proposed SFR is efficient enough to provide better network performance than the spray and wait routing algorithm,in scenarios with sparse node density and fast mobility of the nodes.     

Sort-based relay selection algorithm for decode-and-forward relay system

In this paper, a sort-based relay selection algorithm is proposed for decode-and-forward wireless relay systems. The proposed algorithm can reduce computational complexity and system overhead in the relay selection for practical decode-and-forward wireless relay systems with multiple sources and multiple relays. This would be a very important improvement. Firstly, the sufficient and necessary conditions for a relay to be feasible to a source are derived. By adopting relay transmission via its feasible relay, the source can improve channel capacity compared to direct transmission. Then, a sort-based relay selection algorithm is proposed based on the sufficient and necessary conditions. In the proposed algorithm, each relay makes decision on its feasibility individually, but the final source-relay paring decision is made in a centralized manner. Simulation results show that the proposed algorithm can provide considerable system performance improvement over the existing algorithm. Especially at low signal-to-noise (SNR) region, the performance of the proposed algorithm almost reaches the optimal one.     

Nonlinear optimized adaptive trajectory control of helicopter

This paper attempts to develop an optimized adaptive trajectory control system for helicopters based on the dynamic inversion method. This control algorithm is implemented by three time-scale separation architectures. Pseudo control hedging (PCH) is used to protect the adaptive element from actuator saturation nonlinearities and also from the inner-outer-loop interaction. In addition, to augment the attitude control system, two online adaptive architectures that employ a neural network are used. By tuning the neural network based on the system model, a better and faster learning will be achieved, but this is a frustrating and time consuming process. Due to complexity in accurate tuning of neural network, this paper introduces a non-dominated sorting genetic algorithm II (NSGA-II) for off-line optimization of the neural network. Thus, in the proposed method, the neural network can compensate model inversion error caused by the deficiency of full knowledge of helicopter dynamics more accurately. The effectiveness of proposed method is demonstrated by numerical simulations.     

Common model analysis and improvement of particle swarm optimizer

Particle swarm optimizer （PSO）, a new evolutionary computation algorithm, exhibits good performance for optimization problems, although PSO can not guarantee convergence of a global minimum, even a local minimum. However, there are some adjustable parameters and restrictive conditions which can affect performance of the algorithm. In this paper, the algorithm are analyzed as a time-varying dynamic system, and the sufficient conditions for asymptotic stability of acceleration factors, increment of acceleration factors and inertia weight are deduced. The value of the inertia weight is enhanced to （-1, 1）. Based on the deduced principle of acceleration factors, a new adaptive PSO algorithm- harmonious PSO （HPSO） is proposed. Furthermore it is proved that HPSO is a global search algorithm. In the experiments, HPSO are used to the model identification of a linear motor driving servo system. An Akaike information criteria based fitness function is designed and the algorithms can not only estimate the parameters, but also determine the order of the model simultaneously. The results demonstrate the effectiveness of HPSO.