CDMA系统中的自适应反向闭环功率控制

在码分多址(CDMA)移动通信系统中,反向链路功率控制对克服远近效应和增加系统容量是非常重要的。本文提出了一种基于改进的神经网络(MNN)的自适应闭环功率控制算法,该方法平滑了移动信道衰落的影响,使基站接收到的小区中所有用户的信号功率相等。仿真结果表明,由于神经网络能够较好地识别反向链路的时变特性,MNN功率控制方法比传统的固定步长功率控制方法取得了更好的控制性能和系统容量。     

CDMA系统中自适应反向闭环功率控制

在码分多址（ＣＤＭＡ）移动通信系统中，反向链路功率控制对克服“远近效应”和增加系统容量是非常重要的。本文提出了一种基于改进的神经网络（ＭＮＮ）的自适应闭环功率控制算法，该方法平滑了移动信道衰落的影响。使基站接收到的小区中所有用户的信号功率相等。仿真经结果表明，由于神经网络能够较好地识别反向链路的时变特性，ＭＮＮ功率控制方法比传统的固定步长功率控制方法取得了更好的控制性能和系统容量。     

一种支持QoS的WCDMA混合神经网络反向链路功率控制算法

本文提出了一种混合神经网络反向链路功率控制算法,其外环控制采用Adaline神经网络模型,以不同业务的目标误帧率为指标,实时检测链路的误码状况,调整SIR目标值.内环控制采用Hopfield神经网络模型,通过控制移动台的发射功率确保SIR达到该目标值.与固定步长功率控制算法相比,该算法提高了控制精度和系统的稳定性,能够获得更高的通信质量.仿真实验也验证了算法的有效性.     

一种TD—SCDMA系统中的自适应功率控制算法

功率控制是TD-SCDMA系统无线资源管理的核心技术之一,它对保证系统的高质量通信起着至关重要的作用.为了更好的降低系统传输信息的误比特率(BER),本文针对TD-SCDMA系统的功率控制模块,研究一种适用于反向链路的自适应功率控制算法.通过MATLAB软件仿真的方法,在TD-SCDMA物理层仿真平台上对自适应功率控制算法与定步长功率控制算法进行比较,分析了这两种算法的性能特点.仿真结果表明,自适应功率控制算法有着更好的可行性和有效性,在降低传输误比特率和提高系统性能方面有着实际的效果.     

多业务类分层结构CDMA系统功率控制研究

针对不同QoS要求的多业务类提供支持的分层结构CDMA系统的功率控制进行了研究，给出了该系统的一种功率控制算法。通过对反向链路运用HMRC方法，得到了一种支持多业务类的分层结构CDMA系统中反向链路CIR的简单分析方案和系统容量分析方法。     

CDMA系统中的功率控制

功率控制是CDMA移动通信系统中最为重要的核心技术之一。该文主要介绍CDMA系统反向链路和前向链路的各种功率控制技术。首先介绍功率控制对于CDMA系统的重要性及功率控制的分类，然后分别介绍CDMA系统反向链路和前向链路的功率控制算法。     

基于单个自适应神经元的CDMA反向功率控制算法

CDMA(码分多址)是现代移动通信中的一项重要技术。文章从分析CDMA移动通信的“远近效应”问题着手，提出了一种基于单个自适应神经元(Adaptive Neuron)的反向闭环功率控制算法。该方法能够平滑信道衰落的影响，使基站接收到的小区中所有用户的信号功率基本相等，从而克服反向链路功率控制的“远近效应”并增加系统容量。与传统的固定步长功率控制方法进行仿真比较的结果显示，这种算法具有更快的响应时间，更小的超调量和跟踪误差。     

一种CDMA EVDO系统负荷算法的优化设计

CDMA EVDO系统是一种高速数据传输系统,数据传输本身具有传输突发性和传输速率波动大的特点.同时,由于CDMA EVDO系统前向采用满功率时分复用方式,只有反向采用码分复用方式,所以对于链路负荷的控制只集中在反向链路过载控制上面.针对数据系统,负荷控制算法的难点在于反向链路数据的突发性对系统造成的干扰,以及如何平衡系统吞吐量与抑制反向干扰.针对现有负荷算法自身的特点,文中设计了多条件下的对比测试实验,通过对测试数据进行分析,结合现有算法设计文中提出了一种切实可行的优化负荷算法,能够弥补现有负荷控制算法的不足.     

基于Matlab的CDMA系统功率控制算法研究

对CDMA系统功率进行控制的最根本目的是增加系统容量.以及CDMA系统的干扰及功率与系统容量之间关系,以及移动通信信道(特别是快衰落)对功率控制产生的负面影响,利用Matlab对CDMA系统功率控制算法进行了性能上的仿真.仿真得到慢衰落和快衰落的模拟环境,CDMA系统容量受限于用户问的相互干扰,由反向链路上固定步长功率控制分配算法条件下接收信号的功率的统计图可知,仿真结果与理论分析高度吻合.固定步长功率控制方法的系统稳定性差,且有很大过调量和稳定时间.因此可通过:提高功率测量速度、提高控制量的精调程度两种方法提高控制精度.     

CDMA EV-DO反向链路控制算法与功率控制的结合

EV—DO是CDMA3G的一种高速数据传输技术，它具有独特的反向链路媒体介入控制算法机制，可以参与调整基站反向链路的状况．以优化小区的反向链路情况。除此之外，EV—DO仍然支持反向功率控制。结合两者的特点，共同优化小区反向链路、提高服务质量，是一个需要研究的方向。     

Adaptive fuzzy neural network control for motor-toggle servomechanism

In this study, the dynamic responses of an adaptive fuzzy neural network (FNN) controlled toggle mechanism is described. The toggle mechanism is driven by a permanent magnet (PM) synchronous servo motor. First, based on the principle of computed torque, an adaptive controller is developed to control the position of a slider of the motor-toggle servomechanism. Since the selection of control gain of the adaptive controller has a significant effect on the system performance, an adaptive FNN controller is proposed to control the motor-toggle servomechanism. In the proposed adaptive FNN controller, an FNN is adopted to facilitate the adjustment of control gain on line. Moreover, simulated and experimental results due to a periodic sinusoidal command show that the dynamic behaviors of the proposed adaptive and adaptive FNN controllers are robust with regard to uncertainties.     

Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.     

A novel threshold optimization of ML-CFAR detector in Weibull clutter using fuzzy-neural networks

This paper provides a novel and effective approach based on an adaptive neuro-fuzzy inference system for the solution of constant false alarm rate (CFAR) detection for Weibull clutter statistics. The optimal detection thresholds of the maximum-likelihood CFAR (ML-CFAR) and the Censored ML-CFAR (CML-CFAR) detectors in Weibull clutter with unknown shape parameter are obtained using fuzzy-neural networks (FNN) technique. The theory of the FNN is presented and the genetic learning algorithm (GA) is applied for the training of the FNN threshold estimator. The proposed FNN-ML-CFAR and FNN-CML-CFAR detectors proved to be efficient particularly in the case of spiky clutter. Experimental results showed the effectiveness of an adaptive neuro-fuzzy threshold estimator under different system conditions and it is also shown that the optimal FNN-ML-CFAR and FNN-CML-CFAR detectors can achieve better performances than the conventional ML-CFAR and CML-CFAR algorithms.     

Performance study of close-loop power control algorithms for acellular CDMA system

Performance of a reverse link code-division multiple-access (CDMA) system with fast close-loop power control algorithms is studied. It is found that if the fast close-loop power control algorithm functions effectively, the speed of the mobile unit is in the range such that its Doppler frequency is less than one tenth of the power control updating rate. This paper also proposes a new predictive power control algorithm with better performance in terms of system capacity than the conventional and adaptive step size algorithms. An increase in system capacity as high as 22% compared with the conventional algorithm can be achieved depending on the mobile velocity     

CDMA系统中一种新的基于MMSE多用户检测的自适应功率控制算法

功率控制技术是CDMA通信系统中克服“远一近效应”，降低多址干扰、增大系统容量的一项关键技术。第三代移动通信系统对功率控制提出了新的研究课题。在本文提出CDMA系统中一种新的白适应功率控制和MMSE多用户检测的联合优化的算法，仿真结果表明，这种新的联合优化算法对提高系统容量，改善系统性能有很大的作用。     

Neural Network-Based Kinematic Inversion of Industrial Redundant Robots Using Cooperative Fuzzy Hint for the Joint Limits Avoidance

In this paper, a neural network (NN)-based inverse kinematics problem of redundant manipulators subject to joint limits is presented. The Widrow–Hoff NN with an adaptive learning algorithm derived by applying Lyapunov stability theory is introduced. Since the inverse kinematics has an infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self motion. This FNN is designed on the basis of cooperatively controlling each joint angle in the sense that it stops the motion on the critical axis at its limit at the expense of greater compensation from the most relaxed joint to accomplish the task. Physical constraints such as the joint velocity limits as well as the joint angle limits are incorporated into the method. Experiments are conducted for the PA-10 redundant manipulator to show the effectiveness of the proposed control system. A comparative study is carried out with the gradient projection method.     

TD-SCDMA系统的外环功率控制算法研究

在3GPP协议规范中对外环功率控制算法没有定义,由各设备生产和制造商决定。TD-SCDMA系统每时隙下服务用户数较WCDMA系统每帧下服务用户数明显减少;另外,采用了自适应的智能天线技术,适合WCDMA的外环功率控制算法不能直接适用于TD-SCDMA,提出了一种先进的自适应功率调整步长的外环功率控制算法。由于外环功率控制参数对系统性能影响较大,基于自适应智能天线技术,给出了不同参数下外环功率控制算法的性能,可用于TD-SCDMA网络的规划优化。     

Adaptive rate control algorithm for low power video coding systems

With the recent development of third-generation communication technologies, low power video coding system (such as PDA, Handphone or system on chip) has found wide applications such as live video using a PDA and sharing it among friends, etc. However, video coding in a low power system has two major hurdles to overcome: (1) In a low power system, video coding needs to meet the rigorous constraints of the available memory and computational capacity; (2) In a low power system, the computational power allocated to video coding may vary drastically (in bursts). In this paper, a new adaptive rate control algorithm is proposed for low power video coding system. This adaptive rate control scheme takes into account the time constraint of a low power system, and its bit allocation depends not only on the available data bits, but more importantly, on the available coding time. Experimental results show that, compared to the existing rate control scheme, the new algorithm can always achieve the maximum frame rate, maximize the utilization of the available bandwidth and computing power, increase the average PSNR, and improve the subjective perceptual quality of the reconstructed video.     

Comparison of sliding-mode and fuzzy neural network control formotor-toggle servomechanism

A comparative study of sliding-mode control and fuzzy neural network (FNN) control on the motor-toggle servomechanism is presented. The toggle mechanism is driven by a permanent-magnet synchronous servomotor. The rod and crank of the toggle mechanism are assumed to be rigid. First, Hamilton's principle and Lagrange multiplier method are applied to formulate the equation of motion. Then, based on the principles of the sliding-mode control, a robust controller is developed to control the position of a slider of the motor-toggle servomechanism. Furthermore, an FNN controller with adaptive learning rates is implemented to control the motor-toggle servomechanism for the comparison of control characteristics. Simulation and experimental results show that both the sliding-mode and FNN controllers provide high-performance dynamic characteristics and are robust with regard to parametric variations and external disturbances. Moreover, the FNN controller can result in small control effort without chattering     

Adaptive transmission with discrete code rates and power levels

Throughput maximization of an adaptive transmission system with a finite number of transmission power levels and code rates for communication over slow fading channels is analyzed, based on the concept of information outage. Properties of throughput maximizing policies lead to an iterative algorithm that yields good system designs. Numerical results show that carefully designed discrete adaptive transmission systems with a small number of power levels and code rates can achieve throughput values close to ergodic capacity.