Elegant 342: Modern Communication MATLAB Tutorial

Eleg 342 11modern Communicationmatlab Tutorialdate 20th Nov 2017conte

Eleg 342 11modern Communicationmatlab Tutorialdate 20th Nov 2017conte

ELEG-342-11 Modern Communication MATLAB Tutorial Date: 20th NOV 2017 Content: Amplitude Modulation and Demodulation Frequency Modulation and Demodulation Sampling and Reconstruction of Lowpass Signals Generation PCM Signals Delta Modulation Amplitude Modulation and Demodulation : In our MATLAB program we generate the AM signal with the modulation index of μ=1, By using the message signal m1(t) MATLAB code ExampleAMdemfilt.m generates the message signal, the corresponding AM signal, the rectified signal in noncoherent demodulation, and the rectiï¬ed signal after passing through a low-pass ï¬lter The lowpass ï¬lter at the demodulator has bandwidth of 150 Hz. Amplitude Modulation and Demodulation : (Cont.) Notice the large impulse in the frequency domain of the AM signal No ideal impulse is possible because the window of time is limited, and only very large spikes cantered at the carrier frequency of ±300 Hz are visible.

In addition, the message signal bandwidth is not strictly band-limited. The relatively low carrier frequency of 300 Hz forces the LPF at the demodulator to truncate some message components in the demodulator Amplitude Modulation and Demodulation : (Cont.) Distortion near the sharp comers of the recovered signal is visible. Note: triangl is the function (triangl.m file) used in the MATLAB code, Please make sure that the function is save in the relevant path for access(where you save your MATLAB code) FM modulation and demodulation: In telecommunications and signal processing , frequency modulation (FM) is the encoding of information in a carrier wave by varying the instantaneous frequency of the wave.

This contrasts with amplitude modulation , in which the amplitude of the carrier wave varies, while the frequency remains constant. The FM coefï¬cient is kf = 80 and the PM coefï¬cient is kp = Ï€. The carrier frequency remains 300 Hz. FM modulation and demodulation: (Cont.) The frequency domain responses will have higher bandwidths of the FM and PM signals when compared with amplitude modulations. Note: triangl is the function(triangl.m file) used in the MATLAB code, Please make sure that the function is save in the relevant path for access(where you save your MATLAB code) FM modulation and demodulation: (Cont.) Upon applying the rectifier for envelop detection, we see that the message signal follow closely to the envelope variation of the rectiï¬er output.

Finally, the rectiï¬er output signal is passed through a low—pass ï¬lter with bandwidth 100 Hz. We used the ï¬nite impulse response low—pass ï¬lter of order 80 this time because of the tighter ï¬lter constraint in this example. The FM detector output is then compared with the original message signal. FM modulation and demodulation: (Cont.) The FM demodulation results clearly show some noticeable distortions. First, the higher order low-pass ï¬lter has a much longer response time and delay.

Second, the distortion during the negative half of the message is more severe because the rectiï¬er generates very few cycles of the half—sinusoid. FM modulation and demodulation: (Cont.) This happens because when the message signal is negative, the instantaneous frequency of the FM signal is low. Because we used a carrier frequency of only 300 Hz, the effect of low instantaneous frequency is much more pronounced. If a practical carrier frequency of 100 MHz were applied, this kind of distortion would be completely negligible. FM modulation and demodulation: (Cont.) Practical frequency Demodulator: The differentiator is only way to convert frequency variation of FM signals into amplitude variation that subsequently can be detected by means of envelope detectors.

Zero- crossing detectors are also used because of advance in digital integrated circuits. First step is to use the amplitude limiter to generate the rectangular pulse output The resulting rectangular pulse train of varying width can then be applied to trigger a digital counter. These are the frequency counter designed to measure the instantaneous frequency from the number of zero crossing The rate of zero crossing is equal to the instantaneous frequency of the input signal. Sampling and Reconstruction of Lowpass Signals: In the sampling example, we first construct a signal g(t) with two sinusoidal components of 1 second duration; their frequencies are 1 and 3 Hz. Note, however, that when the signal duration is infinite, the bandwidth of g(t) would be 3 Hz.

However, the finite duration of the signal implies that the actual signal is not bandwidth-limited, although most of the signal content stays within a bandwidth of 5 Hz. For this reason, we select a sampling frequency of 50 Hz, much higher than the minimum Nyquist frequency of 6 Hz. Sampling and Reconstruction of Lowpass Signals: The MATLAB program, Example .m, implements sampling and signal reconstruction. The spectrum of the sampled Signal gT(t) consists of the original signal spectrum periodically repeated every 50 Hz. NOTE: sampandquant and uniquant are function(sampandquant.m and uniquant.m file) used in the MATLAB code, Please make sure that the function is save in the relevant path for access(where you save your MATLAB code) Sampling and Reconstruction of Lowpass Signals: Where: Source or input signal: sig_in = incoming signal L =Quantization level (16 bits encoding) td= original sampling rate ts= new sampling rate s_out= sampled signal sq_out= up sampling (gives the original sampling info) sqh_out= results 16 bit encoding by creating matrix Sampling and Reconstruction of Lowpass Signals: Nonideal Practical Sampling Analysis: Thus far, we have mainly focused on ideal uniform sampling that can use an ideal impulse sampling pulse train to precisely extract the signal value g(kTS) at the precise instant of t = kTs.

In practice, no physical device can carry out such a task. Consequently, we need to consider the more practical implementation of sampling. This analysis is important to the better understanding of errors that typically occur during practical A/D conversion and their effects on signal reconstruction Sampling and Reconstruction of Lowpass Signals: (Cont.) Practical samplers take each signal sample over a short time interval Tp around t = kTs In other words, every Ts seconds, the sampling device takes a short snapshot of duration Tp from the signal g(t) being sampled. This is just like taking a sequence of still photographs of a sprinter during an 100-meter Olympic race. Much like a regular camera that generates a still picture by averaging the picture scene over the window Tp.

Generation PCM Signals: The function sampandquant.m executes both sampling and uniform quantization simultaneously. The sampling period ts is needed, along with the number L of quantization levels. To generate the sampled output s_out, the sampled and quantized output sq_out, and the signal after sampling, quantizing, and zero-order-hold sqh_out. Generation PCM Signals: (Cont.) In the first example, we maintain the 50 Hz sampling frequency and utilize L = 16 uniform quantization levels. The results the PCM Signal.

This PCM signal can be low-pass-filtered at the receiver and compared against the original message signal . The recovered signal is seen to be very close to the original signal g(t). Generation PCM Signals: (Cont.) To illustrate the effect of quantization, we next apply L = 4 PCM quantization levels. It is very clear that smaller number of quantization levels (L = 4) leads to much larger approximation error Delta Modulation: Sample correlation used in DPCM is further exploited in delta modulation (DM) by oversampling (typically four times the Nyquist rate) the baseband signal. This increases the correlation between adjacent samples, which results in a small prediction error that can be encoded using only one bit.

In comparison to PCM and DPCM, it is a very simple and inexpensive method of A/D conversion Delta Modulation: (Cont.) A 1-bit codeword in DM makes word framing unnecessary at the transmitter and the receiver. This strategy allows us to use fewer bits per sample for encoding a baseband signal. To illustrate the effect of DM, the resulting signals from the DM encoder. This example clearly shows that when the step size is too small (Δ1), there is a severe overloading effect as the original signal varies so fast that the small step size is unable to catch up. Delta Modulation: (Cont.) Doubling the DM step size clearly solves the overloading problem in this example.

However, quadrupling the step size (Δ3) would lead to unnecessarily large quantization error. This example thus confirms our earlier analysis that a careful selection of the DM step size is critical. Film #11: Zombieland (2009) Viewing Guide Questions (DO NOT SUBMIT) You will NOT be providing answers/responses to these questions. These questions are simply to draw your attention to specific actions/behaviors/events in the film that relate to course themes and topics. They will also help you to more effectively complete your critical film response. 1. What caused the zombie outbreak? 2. Why don’t the survivors reveal their real names to each other? 3. What is Tallahassee’s “one weaknessâ€? 4. What is the one thing that Columbus fears more than zombies? 5. Why are the sisters (Wichita and Little Rock) going to Pacific Playland amusement park in Los Angeles, and how does Wichita truly feel about this plan? 6. When the survivors first arrive at Bill Murray’s home in the 90210, what sort of activities do they engage in? 7. What happens to Bill Murray, and how do the survivor’s deal with the aftermath? 8. What popular board game do the survivor’s play in front of the fireplace, and how have they altered a key element of the game? 9. Once at Pacific Playland, how do the characters use the amusement park attractions to their advantage in killing zombies? Critical Response #11: Zombieland (2009) Due ONLINE via ELMS by: Tues Nov 28th @11:59 pm 1. Consider how survivors consume during the zombie apocalypse across time. a. What are the different ways that the survivors “consume†while in the Keno Sabe store in Arizona? b. How would the survivors from Dawn of the Dead have acted differently? 2. Fleischer uses an amusement park rather than a mall to make a statement about 21st-century consumerism. a. What statement is Fleischer making about consumerism, and why is the amusement park central to this statement? 3. How do Columbus’ notions of home and family change throughout the film?

Paper For Above instruction

Modern communication systems play a vital role in today's interconnected world, enabling rapid information exchange through various modulation, sampling, and coding techniques. MATLAB provides a robust platform for simulating and analyzing these phenomena, particularly in the context of amplitude modulation (AM), frequency modulation (FM), sampling, and pulse-code modulation (PCM). This paper explores the principles of these techniques, illustrating their implementations and implications in modern telecommunications.

Amplitude Modulation (AM) involves varying the amplitude of a high-frequency carrier wave in proportion to the message signal. Using MATLAB, we generate an AM signal with a modulation index of μ=1, demonstrating key features like the spectrum of the modulated signal and the effects of bandwidth limitations. The MATLAB code 'AMdemfilt.m' exemplifies this process, producing the message signal, the AM signal, and the demodulated output after passing through a low-pass filter with a bandwidth of 150 Hz. The spectrum analysis shows large impulses at the carrier frequency of ±300 Hz, indicating the high frequency components introduced during modulation. Limitations such as bandwidth constraints are evident, leading to distortions near the signal edges, especially when the message bandwidth is not strictly band-limited.

Frequency Modulation (FM), contrasting with AM, encodes information by varying the instantaneous frequency of the carrier wave. MATLAB simulations use an FM coefficient of 80 and a phase modulation coefficient of π, with a carrier frequency at 300 Hz. The frequency domain responses of FM signals often exhibit wider bandwidths due to the nature of frequency variation. The MATLAB implementation demonstrates envelope detection, passing the FM signal through a low-pass filter of order 80 with a bandwidth of 100 Hz, revealing the message signal's envelope. However, higher-order filters introduce response delays, and distortions are more pronounced during negative half-cycles of the message, especially at low carrier frequencies, which would be negligible at higher carrier frequencies such as 100 MHz.

Practical demodulation techniques include differentiation and zero-crossing detection, converting frequency variations into amplitude variations that can be processed by envelope detectors or digital circuits. MATLAB models incorporate these methods, highlighting the importance of amplitude limiters and digital counters for real-time frequency measurement, crucial in modern digital communication systems.

Sampling and reconstruction, fundamental to digital signal processing, are explored through MATLAB simulations that illustrate the effect of finite-duration signals and non-ideal sampling devices. The chosen sampling rate of 50 Hz exceeds the Nyquist rate for the signal components of 1 and 3 Hz, ensuring minimal aliasing. MATLAB functions like 'sampandquant.m' and 'uniquant.m' perform sampling and uniform quantization, producing PCM signals. The simulations show that reducing the number of quantization levels from 16 to 4 introduces significant quantization noise, affecting signal fidelity. The reconstruction process, involving zero-order hold and low-pass filtering, demonstrates how realistic sampling constraints impact the recovered signal fidelity.

Delta modulation (DM), a simplified A/D conversion technique utilizing oversampling and small step sizes, is examined through MATLAB examples. It encodes the signal with a single bit, exploiting high correlation between adjacent samples. Proper step size selection is critical to avoid overloading or excessive quantization error. MATLAB results illustrate the delicate balance needed in step size adjustment, emphasizing DM's efficiency and limitations, particularly for rapidly varying signals.

These digital modulation and sampling techniques have profound implications for modern telecommunication systems, supporting efficient, robust, and cost-effective data transmission. They accommodate the increasing demand for bandwidth, minimize error contamination, and enable reliable digital signal processing across diverse applications. MATLAB simulations remain essential tools for visualizing and optimizing these processes, advancing our understanding of complex modulation strategies and their practical challenges in real-world systems.

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