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Cable Modems
Published in Keshab K. Parhi, Takao Nishitani, Digital Signal Processing for Multimedia Systems, 2018
The issue of spectral shaping is quite interesting because it potentially could provide low cost dB gain. Spectral shaping is a technique that makes the signal output look more Gaussian. This has the effect of bringing the signal closer to the ideal Shannon theory signal and consequently decreases the required transmit power. The gain in power can be as high as 1.53dB [40] and the complexity of implementation of a shaping system is practically all in the transmitter [41] [42], It therefore seems ideal for use in a broadcast system where there is one transmitter for many receivers. Unfortunately there is an implementation problem in the receiver with transmitting a Gaussian distributed set of symbols. The most commonly used blind equalization techniques which are used to adaptively determine the filtering required to remove the channel distortion, do their job by extracting information about higher order statistics from the received signal. If the received signal is Gaussian its higher order statistics are deterministically dependent on its first and second order statistics and these algorithms will not work [43]. Therefore spectral shaping has not as yet been used in broadcast systems.
Introduction
Published in K. C. Raveendranathan, Neuro-Fuzzy Equalizers for Mobile Cellular Channels, 2017
It is established that Blind Equalization is more suitable for broadcast channels like the mobile cellular channels.1 Blind equalization is applied to eliminate the channel distortion and multipath effects since the transmitted signal is unknown at the receiver end. The purpose of blind channel equalization is to remove ISI caused by time dispersion in the channel response without resorting to an explicit knowledge of the channel characteristics or the channel input sequence (Dogancay and Kennedy 1999). The interest in blind equalization using RBF neural networks has been revived by Nan Xie (Xie and Leung 2005). Recently some authors have contributed considerably in the construction of equalizers for broadcast channels. Blind equalization using Pseudo-Gaussian based Compensatory Neuro-Fuzzy Filter (CNFF) was one such approach (Lin and Ho 2003, Lin and Juang 1996). We can adapt the CNFF for the indoor mobile cellular channel. Lambert et al. (1996) described an adaptive block decision feedback receiver for improved performance in channels with severe ISI. The theory of an Adaptive Network-based Neuro-Fuzzy Inference System (ANFIS) and its application in nonlinear problem solving was first suggested by Jang in his seminal paper (Jang 1993). Several channel models are discussed in (IEEE 802.16 BWA WG 2000). This led to several new strides in system identification and control system design. Design and simulation of mobile channel equalizers based on ANFIS is one of the major areas of focus of this book.
Smart Antennas for Mobile Autonomous Systems
Published in Jitendra R. Raol, Ajith K. Gopal, Mobile Intelligent Autonomous Systems, 2016
In digital communications, an equalizer that can fully cancel the interference from a linear dispersive medium within a finite time span while not amplifying the noise is bound to be popular. Another point of consideration is that it can be implemented with ease. Equalization is important for the receiver of communication systems to correctly recover the symbols sent by the transmitter, because the received signals may contain interference, noises and so on. Many real communication devices contain equalization modules, such as modems, cellular phones and digital TVs. Blind equalization is useful to cancel the repeatedly transmitted training signals so as to improve system output. It is still a challenging work to find an effective, robust, computationally efficient algorithm to do blind equalization [5].
Torque Coordinated Control of Hybrid Power System Based on Feedback Iterative Algorithm in Industry 4.0
Published in IETE Journal of Research, 2022
Yunlong Wang, Hongtian Zhang, Yuantao Sun
Hybrid system characteristics of signal comprise distinguished signal values. The orthogonal wavelet transform signal under the description of the singular values conforms to the monotone interval and the characteristics of the high-order iterative signal frequency amplitude of gradient vector module between and index also have the correlation. This connection will have a certain influence on the higher-order iteration of the signal value. The intrinsic connection between the two is shown as given in Equations (7) and (8): Blink equalization has been performed on the extracted detail features of the torque signal of the hybrid power system, so that the system can be more suitable for approximating the optimal solution obtained within the monotone interval. The minimum value of within the monotone interval is solved as given in Equation (9): Thus, the edge characteristics of the torque signal of the hybrid power system and its Gaussian convolution results under multi-scale amplitudes can be calculated as given in Equation (10): To improve the blind equalization, the singular value in the monotonic interval of the singular value should be iteratively optimized. The larger value of the torque signal indicates that the final singular value is closer to the optimal solution. The orthogonal wavelet transform eliminates most of the environmental noise and Gaussian noise. It extracts the feature of the torque signal and processes it blindly. Finally, the sine wave of each torque signal is decomposed into several monotonous iterative intervals for approximating the theoretical solution. The high-order monotone feedback iterative algorithm has a strong anti-disturbance ability and is stable to signal edge processing. The eigenvalue of the signal is closer to the theoretical value and contains more useful information.