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PDF | In this paper, basic components of a digital communication The simulation program is modular and flexible to incorporate any and systems and related courses using the MATLAB Graphical User Interface (GUIs). Multi-Carrier Digital Communications: Theory and Applications of OFDM. Ahmad R. S. Bahai and . The Application of Simulation to the Design of Communication Systems. Methods of Generating Random Numbers from an Arbitrary pdf. both s(x) and its second derivative were obtained using MATLAB. It can be. Simulation of Digital Communication Systems Using Matlab - Mathuranathan algorithm used in PDF documents [ZivMay], [ZivSep], [Welch].

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Digital Matched Filter and Slicer. Monte Carlo Simulation. MATLAB Simulation. ▷ Objective: Simulate a simple communication system and estimate bit error rate. Digital Communication Systems using MATLAB® and Simulink® utilizes a communication systems simulator by The MathWorksTM (narledikupttemp.ml) with. Simulation of Digital Communication Systems Using Matlab [eBook] download 2 formats at same retail price: PDF (for viewing on PC) and EPUB.

Save for Later About this Item Featuring a variety of applications that motivate students, this book serves as a companion or supplement to any of the comprehensive textbooks on communication systems. By design, the treatment of the various topics is brief. The authors provide the motivation and a short introduction to each topic, establish the necessary notation, and then illustrate the basic concepts by means of an example. Contents, 1. Fourier Series. Fourier Transforms. Power and Energy. Lowpass Equivalent of Bandpass Signals.

Can be considered as a discrete-time system. Minor problem: input and output operate at different rates. Input signal is up-sampled by factor f s T to make input and output rates equal. Insert f s T 1 zeros between input samples.

Note that E s is controlled by adjusting the gain A at the transmitter. We proceed in three steps: 1. Establish parameters describing the system, By parameterizing the simulation, other scenarios are easily accommodated. Simulate discrete-time equivalent system, 3. Collect statistics from repeated simulation. The parameters set in the controlling script are passed as inputs. The body of the function simulates the transmission of the signal and subsequent demodulation. The number of incorrect decisions is determined and returned.

In a real system, these samples would be processed by digital hardware to recover the transmitted bits. The first function performed there is digital matched filtering. This is a discrete-time implementation of the matched filter discussed before.

The matched filter is the best possible processor for enhancing the signal-to-noise ratio of the received signal.

Paris ECE 18 Digital Matched Filter In our simulator, the vector Received is passed through a discrete-time matched filter and down-sampled to the symbol rate. The impulse response of the matched filter is the conjugate complex of the time-reversed, discrete-time channel response h[n].

This function is performed by the slicer. The operation of the slicer is best understood in terms of the IQ-scatter plot on the previous slide. The red circles in the plot indicate the noise-free signal locations for each of the possibly transmitted signals. For each output from the matched filter, the slicer determines the nearest noise-free signal location.

The decision is made in favor of the symbol that corresponds to the noise-free signal nearest the matched filter output. Some adjustments to the above procedure are needed when symbols are not equally likely.

The operation of the simulator is controlled via the parameters passed in the input structure. The body of the function is shown on the next slide; it consists mainly of calls to functions in our toolbox. The objective of the Monte Carlo simulation is to estimate the symbol error rate our system can achieve. The idea behind a Monte Carlo simulation is simple: Simulate the system repeatedly, for each simulation count the number of transmitted symbols and symbol errors, estimate the symbol error rate as the ratio of the total number of observed errors and the total number of transmitted bits.

Answer: It depends on the desired level of accuracy confidence , and most importantly on the symbol error rate. Confidence Intervals: Assume we form an estimate of the symbol error rate P e as described above.

The parameter s c is called the confidence interval; it depends on the confidence level p c, the error probability P e, and the number of transmitted symbols N. Paris ECE 30 Choosing the Number of Simulations For a Monte Carlo simulation, a stop criterion can be formulated from a desired confidence level p c and, thus, z c an acceptable confidence interval s c, the error rate P e.

A Monte Carlo simulation can be stopped after simulating N transmissions. Then, it is desirable to specify the confidence interval as a fraction of the error rate. Recognize that P e N is the expected number of errors! Relationship between original, continuous-time system and discrete-time equivalent was established. Digital post-processing: digital matched filter and slicer.

Monte Carlo simulation of a simple communication system was performed. Close attention was paid to the accuracy of simulation results via confidence levels and intervals. Derived simple rule of thumb for stop-criterion. 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.

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. Khalid Sayood.

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Computational Lithography. Xu Ma. Practical Digital Signal Processing. Edmund Lai. Applied Digital Signal Processing.

Dimitris G. Fractional-order Modeling and Control of Dynamic Systems. Aleksei Tepljakov. Thomas L. Erik Dahlman. Chiara Buratti. Smartphone Energy Consumption. Sasu Tarkoma. Richard Swale. Stefan M. Heterogeneous Cellular Networks.

Dr Xiaoli Chu. Shahid Mumtaz. Amitabha Ghosh. Modulation and Coding Techniques in Wireless Communications. Evgenii Krouk.