MATLAB/SIMULINK FOR DIGITAL COMMUNICATION EBOOK
Editorial Reviews. From the Back Cover. A comprehensive and detailed treatment of the eBook features: Highlight, take notes, and search in . Back. Digital Modulations using Matlab: Build Simulation Models from Scratch · Mathuranathan. MATLAB /Simulink for Digital Communication. Pages Similar Free eBooks. Filter by Wireless Communications with Matlab and Simulink: IEEE As of today we have 78,, eBooks for you to download for free. No annoying ads, no download limits, MATLAB /Simulink for Digital Communication.
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weinratgeber.info with modern digital communications in the MATLAB and Simulink simulation environment. An extensive. Are you interested in simulation of communication systems in Matlab and do not Simulation of Digital Communication Systems Using Matlab ebook by . Problem-Based Learning in Communication Systems Using MATLAB and Simulink. MATLAB/Simulink for Digital Communication, Written for students and engineers, this book provides a reference for studying communication systems. The aim of.
Essentially it is the other name for compression. All data compression techniques can be classified under two categories namely lossless compression techniques and lossy compression techniques. In lossless compression the exact original data can be reconstructed from compressed data.
But in lossy compression some errors exist after de-compression, but those errors are not obvious or perceivable. A Few lossless and lossy compression techniques are listed below. The next block in a communication system is the channel coding block. There is an important difference between channel coding and source coding. Source coding attempts to compress the data to improve bandwidth utilization, whereas, channel coding attempts to add redundancy to the data to make it more reliable which reduces data rate and therefore more robust against the channel noise.
Simulation of Digital Communication Systems Using Matlab [eBook] – Second Edition
Channel coding reduces the data rate and improves the reliability of the system. Several models of channels were developed to design a communication system according to the possible type of channel one may use. Two of them are listed here. In this model, the transmitter sends a bit and the receiver receives it.
Suppose if there exists a probability for this bit getting flipped, then it is referred to as a Binary Symmetric Channel. This situation can be diagrammatically represented as shown in following figure. In this model, the channel noise is assumed to have Gaussian nature and is additive.
Compared to other equivalent channels, the AWGN channel does the maximum bit corruption and the systems designed to provide reliability in AWGN channel is assumed to give best performance results in other real-world channels. But the real performance may vary. The AWGN channel is a good model for many satellite and deep space communication links.
In serial data communications, the AWGN mathematical model is used to model the timing error caused by random jitter. The distortion incurred by transmission over a lossy medium is modeled as the addition of a zero-mean Gaussian random value to each transmitted bit. This error correction technique is used to send data over unreliable noisy channels. The transmitted information is added with redundant bits using Error Correction Coding ECC , otherwise called channel coding.
This approach allows us to detect and correct the bit errors in the receiver without the need for retransmission. It is important to bear in mind that the correction and detection of errors are not absolute but rather statistical.
Thus, one of our goals is to minimize the BER Bit Error Rate given a channel with certain noise characteristics and bandwidth.
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The difference N-K represents the number of redundant bits added to the informational bits. Error Control Coding techniques are used to produce the code words from the information bits.
The codewords carry with them an inherent potential to certain extent to recover from the distortions induced by the channel noise. The corresponding decoding technique in the receiver uses the redundant information in the codeword and tries to restore the original information, thereby providing immunity against the channel noise.
There are two general schemes for channel coding: To keep things simple, we will not go into the jungle of advanced channel coding techniques. Nyquist-Shannon Sampling Theorem is the fundamental base over which all the digital processing techniques are built. Processing a signal in digital domain gives several advantages like immunity to temperature drift, accuracy, predictability, ease of design, ease of implementation etc..
In analog domain, the signal that is of concern is continuous in both time and amplitude. The process of discretization of the analog signal in both time domain and amplitude levels yields the equivalent digital signal.
The sampling operation samples chops the incoming signal at regular intervals called Sampling Rate denoted by TS. Consult the Nyquist-Shannon Sampling Theorem to select the sampling rate or sampling frequency. If the signal is confined to a maximum frequency of F m Hz, in other words, the signal is a baseband signal extending from 0 Hz to maximum Fm Hz.
For a faithful reproduction and reconstruction of an analog signal that is confined to a maximum frequency Fm, the signal should be sampled at a Sampling frequency FS that is greater than or equal to twice the maximum frequency of the signal.
Consider a 10Hz sine wave in analog domain. That is, we are free to choose any number above 20 Hz. Higher the sampling frequency higher is the accuracy of representation of the signal. Higher sampling frequency also implies more samples, which implies more storage space or more memory requirements.
In time domain, the process of sampling can be viewed as multiplying the signal with a series of pulses "pulse train at regular intervals — TS. If we want to convert the sampled signal back to analog domain, all we need to do is to filter out those unwanted frequency components by using a reconstruction filter In this case it is a low pass filter that is designed to select only those frequency components that are up to Fm Hz. The above process mentions only the sampling part which samples the incoming analog signal at regular intervals.
Actually a quantizer will follow the sampler which will discretize quantize amplitude levels of the sampled signal. The quantized amplitude levels are sent to an encoder that converts the discrete amplitude levels to binary representation binary data.
So when converting the binary data back to analog domain, we need a Digital to Analog Converter DAC that converts the binary data to analog signal. Now the converted signal after the DAC contains the same unwanted frequencies as well as the wanted component.
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Thus a reconstruction filter with proper cut-off frequency has to be placed after the DAC to filter out only the wanted components. We do not need to care about the interference that occurs at 20Hz since it is a noise and any way it has to be eliminated.
Aliasing depends on the sampling frequency and its relationship with the frequency components. Actually the aliasing zones occur on the either sides of 0. The following figure illustrates the concept of aliasing zones. In the above figure, zone 2 is just a mirror image of zone 1 with frequency reversal.
Similarly zone 2 will create aliases in zone 3 without frequency reversal ; zone 3 creates mirror image in zone 4 with frequency reversal and so on…. Once the aliasing components enter our band of interest, it is impossible to distinguish between original components and aliased components and as a result, the original content of the signal will be lost. This is achieved by using an anti-aliasing filter that precedes the analog to digital converter.
Thus, a complete design of analog to digital conversion contains an anti-aliasing filter preceding the ADC and the complete design of digital to analog conversion contains a reconstruction filter succeeding the DAC.
Remember that both the anti-aliasing and reconstruction filters are analog filters since they operate on analog signals.
So it is imperative that the sampling rate has to be chosen carefully to relax the requirements for the anti-aliasing and reconstruction filters. In this case we are over-sampling the signal.
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John H. When a QAM modulator output is the input to a nonlinear device, the input voltage is backed off to force the modulated waveform to remain within the linear portion of the device. Problem sets at the end of each chapter accompany topics to be emphasized.
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