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.>