We investigate a new application of the well-known spread spectrum code division multiple access (SS-CDMA) techniques to multimedia services related to the development of the next-generation wireless mobile networks interconnecting with a wireline ATM-based broadband network. Such services allow users to share novel multimedia applications without any geographical restrictions. However, since the mobile radio channel has a fixed limited bandwidth, the traditional SS-CDMA system may not be sufficient to accommodate the variable bit rate (VBR) multimedia services requested by multiple mobile users simultaneously. Moreover, the traffic load at the base station can change dynamically due to the time-varying throughput requirement of these requested multimedia services. To tackle this difficulty, a multicode CDMA (MC-CDMA) technique is proposed to provide multirate multimedia services by varying the number of spreading codes assigned to each user in order to meet its throughput requirement. In MC-CDMA, a spreading code can be used to transmit information at a basic bit rate. Users (video or data) who need higher transmission rates can use multiple codes in parallel. Meanwhile, the maximum available number of codes in the MC-CDMA system is still limited. Hence, a cost-effective dynamic code allocation scheme has then been proposed to dynamically assign an appropriate number of codes to each user for achieving the maximum resource utilization for multiuser multimedia services via the mobile radio channel. Finally, a number of real multimedia titles generated from the well-known MacroMind Director are conducted to evaluate performance.

The process of eliminating the color errors from the gamut mismatch, resolution conversion, and nonlinearity between scanner and printer is usually recognized as an essential issue of color reproduction. This paper presents a new formulation based on the generalized inverse plant control for the color error reduction process. In our formulation, the printer input and scanner output correspond to the input and output of a system plant, respectively. Obviously, if the printer input equals the scanner output, then there are no color errors involved in the entire system. In other words, the plant becomes an identity system. To achieve this goal, a plant generalized inverse should be identified and added to the original system. Since the system of a combination of both scanner and printer is highly nonlinear, CMAC-based neural networks, which have the capability to learn arbitrary nonlinearity, are applied to identify the plant generalized inverse. CMAC network is a perceptron-like feedforward structure with associative memory properties. Its memory requirements can be greatly reduced by the use of hash coding techniques. In order for CMAC networks to construct high-order, smooth, nonlinear plant inverse, more general CMAC addressing schemes have been proposed in conjunction with use of B-spline receptive functions. It is shown that B-spline CMAC networks learn orders of magnitude more rapidly than typical implementations of back propagation in the multilayered neural networks, due to the local nature of its weighting updating and the finite support of B-spline receptive field functions. Finally, a number of test samples are conducted to verify the effectiveness of the proposed method.

We present a generic ATM-based multicode code division multiple access (CDMA) transport architecture for the Motion Picture Experts Group phase 2 (MPEG-2) compressed video services over a bandlimited mobile channel with the emphasis of wireless asynchronous transfer mode (ATM) cell design, spreading code management, and the impact of CDMA systems on the video services. Such services allow users to share novel MPEG-2 video applications without any geographical restrictions. The ATM technique is especially well suited for variable bit rate (VBR) MPEG-2 video because of its ability to allocate bandwidth on demand to these services. However, since the mobile radio has a limited channel capacity, the overall capacity of the traditional ATM-based CDMA system may not be sufficient to accommodate the MPEG-2 video services requested by the multiple mobile users simultaneously. To tackle this difficulty, a multicode CDMA technique is proposed to provide multiple VBR MPEG-2 video services by varying the number of spreading codes assigned to each MPEG-2 video in order to meet its dynamic throughput requirement. Moreover, both the multipath fading and interference on CDMA radio channels tend to cause significant transmission error and MPEG-2 bit streams are very vulnerable to these errors. Powerful forward error connection (FEC) codes are therefore necessary to protect the video data so that it can be successfully transmitted at acceptable signal power level. Two separate FEC code schemes are applied to the header and payload of an ATM cell containing MPEG-2 video data, respectively. The ATM cell header is protected by a relatively powerful FEC code to ensure the low average cell loss rate (CLR). On the other hand, the ATM cell payload is encoded for varying degrees of error protection according to both the priority and statistical behavior of the payload data in MPEG-2 videos. An adaptive FEC code combining scheme is proposed to provide good protection cell payload data with the maximization of its code rate in order to minimize the extra bandwidth for FEC overhead. Two typical MPEG2 test sequences are conducted to evaluate the effectiveness of our system.

This paper investigates the application of a pipelined recurrent neural network (PRNN) to the adaptive traffic prediction of MPEG video signal via dynamic ATM networks. The traffic signal of each picture type (I, P, and B) of MPEG video is characterized by a general nonlinear autoregressive moving average (NARMA) process. Moreover, a minimum mean-squared error predictor based on the NARMA model is developed to provide the best prediction for the video traffic signal. However, the explicit functional expression of the best mean-squared error predictor is actually unknown. To tackle this difficulty, a PRNN that consists of a number of simpler small-scale recurrent neural network (RNN) modules with less computational complexity is conducted to introduce the best nonlinear approximation capability into the minimum mean-squared error predictor model in order to accurately predict the future behavior of MPEG video traffic in a relatively short time period based on adaptive learning for each module from previous measurement data, in order to provide faster and more accurate control action to avoid the effects of excessive load situation. Since those modules of PRNN can be performed simultaneously in a pipelined parallelism fashion, this would lead to a significant improvement in the total computational efficiency of PRNN. In order to further improve the convergence performance of the adaptive algorithm for PRNN, a learning-rate annealing schedule is proposed to accelerate the adaptive learning process. Another advantage of the PRNN-based predictor is its generalization from learning that is useful for learning a dynamic environment for MPEG video traffic prediction in ATM networks where observations may be incomplete, delayed, or partially available. The PRNN-based predictor presented in this paper is shown to be promising and practically feasible in obtaining the best adaptive prediction of real-time MPEG video traffic.

This paper investigates the application of subcarrier multiplexed code-division-multiple-access (CDMA) techniques to image transmission over fiber-optic local-area networks (LANs). In the hybrid scheme, CDMA is used to suppress the interference caused by the laser nonlinearity in the subcarrier multiplexing (SCM) fiber-optic communication systems. Likewise, the SCM scheme is able to increase the channel data rate of CDMA systems. This hybrid system combines the advantages of both schemes and is particularly well suited to subband coding that divides the image information into multiple parallel data streams using an analysis filter bank, each of which is transmitted via a unique subcarrier-code pair, where the spreading code and subcarrier frequency correspond to the image and one of its subbands, respectively. This hybrid scheme also allows more than one image to be transmitted and be accessed simultaneously at the same channel bandwidth, in which each image is assigned a particular spreading code added to its digital data modulating the subcarrier. After transmission, each received signal is independently recovered at a high-Q surface acoustic wave (SAW) receiver with the matching subcarrier-code pair. Other concurrent signals are rejected by the SAW. Then, all the recovered subbands are reassembled by a synthesis filter bank into a close reproduction to the original image. The image quality of subband image transmission via CDMA/SCM fiber-optic channels is evaluated and examined.

We investigate the use of fuzzy logic control techniques for adaptive power control in a direct-sequence code-division multiple-access (DS/CDMA) cellular system over the mobile fading radio channels with propagation time delay. A fuzzy PI (proportional-plus-integral) control whose input variables are the received power error and error change is introduced to determine each user transmitting power in order to simultaneously equalize all users' signal powers received at the base station and achieve better system stability and control performance. This control strategy can ensure long loop transmission delays without causing the system to become unstable. According to the well-known phase-plane method, the derivation of the fuzzy PI control has been carried out by analyzing the response areas, cross-over, and extreme points of the system step response with apriori knowledge of the dynamics of the CDMA mobile fading channels. For a long time-delay fading process, a methodology is developed to modify the rule base to contain the delay information for reducing the deadtime effects of the process. Moreover, the additional advantages of fuzzy PI control over conventional control theories are: increased robustness in spite of interference and the ability to handle the time-delay process whose parameters are not accurately known. Simulation results show that a good performance can be achieved both in RMS tracking error and traffic capacity by use of fuzzy PI power control, especially in poor interuser interference and long time delay conditions.

As demand for networked multimedia applications is increasing rapidly, it is important to provide the ubiquitous accessibility for these services in wireless communication environment. Such access allows users to share novel multimedia applications without any geographical restrictions. We investigate the application of the well-known antimultipath spread spectrum code-division multiple access (SS-CDMA) techniques to image transmission related to the development of next-generation multimedia wireless local area networks within a building. SS-CDMA is particularly well suited to a subband coding scheme that divides the image information into multiple parallel data streams using an analysis filter bank, each of which is multiplied by its unique spreading code. All the product signals are then transmitted at the same time in the same radio channel, even though the total bandwidth of all the signals may exceed the channel bandwidth. Each received signal is independently recovered at the decoder by multiplying its spreading code and all the recovered subbands are then reassembled by a synthesis filter bank into a close reproduction to the original image. A forward error correction (FEC) scheme based on convolutional codes with interleaving is proposed to minimize the effect of bursty channel errors on the picture quality degradation. Better image quality can be achieved by using the predetection diversity combining in order to combat the indoor multipath distortion. In this paper, the image quality of subband image transmission via SS-CDMA indoor fading channels with differential phase shift keying (DPSK) modulation is evaluated and examined. Simulation results show that the image peak signal-to-noise (PSNR) ratio is inversely related to a logarithmic function of bit error rate when each subband employs equal protection channel coding.

This paper introduces an adaptive decision feedback equalization using the multilayer perceptron structure of an M-ary PSK signal through a TDMA satellite radio channel. The transmission is disturbed not only by intersymbol interference (ISI) and additive white Gaussian noise, but also by the nonlinearity of transmitter amplifiers. The conventional decision feedback equalizer (DFE) is not well-suited to detect the transmitted sequence, whereas the neural-based DFE is able to take into account the nonlinearities and therefore to detect the signal much better. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To overcome this difficulty, a neural-based DFE is proposed to deal with the complex PSK signal over the complex-valued nonlinear MPSK satellite channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, a modified back-propagation algorithm with better convergence properties is derived on the basis of delta-bar-delta rule. Simulation results for the equalization of QPSK satellite channels show that the neural-based DFE provides a superior bit error rate performance relative to the conventional mean square DFE, especially in poor signal-to-noise ratio conditions.>

Separating a color signal into illumination and surface reflectance components is a fundamental issue in color reproduction and constancy. This can be carried out by minimizing the error in the least squares (LS) fit of the product of the illumination and the surface spectral reflectance to the actual color signal. When taking in account the physical realizability constraints on the surface reflectance and illumination, the feasible solutions to the nonlinear LS problem should satisfy a number of linear inequalities. Four distinct novel optimization algorithms are presented to employ these constraints to minimize the nonlinear LS fitting error. The first approach, which is based on Ritter's superlinear convergent method (Luengerger, 1980), provides a computationally superior algorithm to find the minimum solution to the nonlinear LS error problem subject to linear inequality constraints. Unfortunately, this gradient-like algorithm may sometimes be trapped at a local minimum or become unstable when the parameters involved in the algorithm are not tuned properly. The remaining three methods are based on the stable and promising global minimizer called simulated annealing. The annealing algorithm can always find the global minimum solution with probability one, but its convergence is slow. To tackle this, a cost-effective variable-separable formulation based on the concept of Golub and Pereyra (1973) is adopted to reduce the nonlinear LS problem to be a small-scale nonlinear LS problem. The computational efficiency can be further improved when the original Boltzman generating distribution of the classical annealing is replaced by the Cauchy distribution.

This paper investigates the application of the multilayer perceptron structure to the packet-wise adaptive decision feedback equalization of a M-ary QAM signal through a TDMA indoor radio channel in the presence of intersymbol interference (ISI) and additive Gaussian noise. Since the multilayer neural networks are capable of producing complex decision regions with arbitrarily nonlinear boundaries, this would greatly improve the performance of conventional decision feedback equalizer (DFE), where the decision boundaries of conventional DFE's are linear. Nevertheless, the applications of the traditional multilayer neural networks have been limited to real-valued signals. To tackle this difficulty, a neural-based DFE is proposed to deal with the complex QAM signal over the complex-valued fading multipath radio channel without performing time-consuming complex-valued back-propagation training algorithms, while maintaining almost the same computational complexity as the original real-valued training algorithm. Moreover, this neural-based DFE trained by packet-wise back-propagation algorithm would approach an ideal equalizer after receiving a sufficient number of packets. The results show that the neural-based DFE provides a superior bit-error rate performance relative to the conventional mean square DFE, especially in poor signal to noise ratio conditions.>