The mixer takes in a signal, outputs the down/up-converted signal, and has a third port which is used to feed in an oscillator. 1995 Conference Record of the Twenty-Ninth Asilomar Conference on, vol. Using your mobile phone camera - scan the code below and download the Kindle app. Digital Modulations using Matlab by Mathuranathan Viswanathan Synopsis Digital Modulations using Matlab is a learner-friendly, practical and example driven book, that gives you a solid background in building simulation models for digital modulation systems in Matlab. Now because they always travel at the same speed, the distance the wave travels in one full oscillation (one full cycle of the sine wave) depends on its frequency. These built-in functions or toolboxes hide a lot of background computations. Simulation for ascertaining performance of digital modulation techniques in AWGN and fading channels - Eb/N0 Vs BER curves. Design and implementation of linear equalizers - zero forcing and MMSE equalizers, using them in a communication link, LMS algorithm for adaptive equalization. Simulation and performance of modulation systems with receiver impairments. Examples using object oriented programming. Simulation scripts using SciPy, Numpy and Matplotlib packages. Mathuranathan Viswanthan is the author of a popular technical blog gaussianwaves.com that elaborates on signal processing for communication systems. Bring your club to Amazon Book Clubs, start a new book club and invite your friends to join, or find a club thats right for you for free. Free shipping for many products! For example, we could adjust I and Q in a way that keeps the amplitude constant and makes the phase whatever we want. When we covered Fourier series and FFTs last chapter, we had not dived into complex numbers yet. Monson H. Hayes , Statistical Digital Signal Processing and Modeling, chapter 4.4.5, Application FIR least squares inverse filter, Wiley, 1 edition, April 11, 1996. 216 p. There exist many textbooks that provide an in-depth treatment of various topics in digital modulation techniques. ');ax[1,0].set_title('IQ Phase mismatch only') ax[1,1].plot(real(s),imag(s),'b.') With reference to Figure 6.2, let r = ri + jrq denote the perfect unimpaired signal and z = zi + jzq represent the signal with IQ imbalance. We will talk about the shortly. The output will be 1024 complex floats. All electromagnetic waves travel at the speed of light, which is about 3e8 m/s, at least when traveling through air or a vacuum. We will use for the cos() and for the sin(): We can see this visually by plotting I and Q equal to 1: We call the cos() the in phase component, hence the name I, and the sin() is the 90 degrees out of phase or quadrature component, hence Q. Also shown is an example bandpass signal, centered at a very high frequency denoted . A third architecture, one that is popular because its how old radios worked, is known as superheterodyne. That means we would have to sample at 4.8 GHz, as we learned. : digital, Program 78: DigiCommPy\compensation.py: Model for compensating DC offsets in the IQ branches import numpy as np from numpy import mean,real,imag,sign,abs,sqrt,sum def dc_compensation(z): """ Function to estimate and remove DC impairments in the IQ branch Parameters: z: DC impaired signal sequence (numpy format) Returns: v: DC removed signal sequence """ iDCest=mean(real(z)) # estimated DC on I branch qDCest=mean(imag(z)) # estimated DC on I branch v=z-(iDCest+1j*qDCest) # remove estimated DCs return v 6.3 IQ imbalance model 193 6.3 IQ imbalance model Two IQ imbalance models, namely, single-branch IQ imbalance model and double-branch IQ imbalance model, are used in practice. There will always be a sample rate, the rate at which sampling is performed. I do DSP a bit but not enough to ever be able to decipher the usual DSP Mathuranathan is a wonderfully clear writer. 1 review Get A Copy Amazon Stores Libraries Paperback, Grayscale, 216 pages Published December 2nd 2019 by Independently published More Details. Program 83: DigiCommPy\chapter 6\rf impairments.py: Visualizing receiver impairments import numpy as np #for numerical computing from numpy import real,imag from DigiCommPy.modem import QAMModem #QAM Modem model from DigiCommPy.impairments import ImpairmentModel #Impairment Model 198 6 Receiver Impairments and Compensation import matplotlib.pyplot as plt #for plotting functions M=64 # M-QAM modulation order nSym=1000 # To generate random symbols # uniform random symbols from 0 to M-1 inputSyms = np.random.randint(low=0, high = M, size=nSym) modem = QAMModem(M) #initialize the M-QAM modem object s = modem.modulate(inputSyms) #modulated sequence impModel_1 impModel_2 impModel_3 impModel_4 #Add r1 = r2 = r3 = r4 = = = = = ImpairmentModel(g=0.8) # gain mismatch only model ImpairmentModel(phi=12) # phase mismatch only model ImpairmentModel(dc_i=0.5,dc_q=0.5) # DC offsets only ImpairmentModel(g=0.8,phi=12,dc_i=0.5,dc_q=0.5) # All impairments impairments to the input signal sequence using the models impModel_1.receiver_impairments(s) impModel_2.receiver_impairments(s) impModel_3.receiver_impairments(s) impModel_4.receiver_impairments(s) fig, ax = plt.subplots(nrows=2,ncols = 2) ax[0,0].plot(real(s),imag(s),'b.') Mathuranathan uses non-trivial examples that are really useful and practical to learn from. Both these techniques are derived for the single-branch IQ imbalance model. Digital-to-Analog signals is the next conversion we will discuss in this chapter. 6.1: Direct conversion image rejection receiver Fig. T.H. This item cannot be shipped to your selected delivery location. In practice our sample rates will be on the order of hundreds of kHz to tens of MHz or even higher. x Digital Signal Processing with Python Programming Statistical inferences The second chapter is devoted to statistical inference. Asecuritysite.com. We talked about how the FFT figures out which frequencies exist in that set of samples (the magnitude of the FFT indicates the strength of each frequency). Let z = zi + jzq , represent the IQ imbalanceimpaired complex baseband signal, that needs to be compensated. The figure in the Receiver Side section demonstrates how the input signal is downconverted and split into I and Q. Link to the black & white edition: https://www.amazon.com/dp/1712342746, Publisher Your average DSP textbook will discuss sampling, but it tends not to include implementation hurdles such as DC offsets despite their prevalence in practice. Read instantly on your browser with Kindle Cloud Reader. Digital Modulations using Python: (Color edition) : Srinivasan, Varsha, Viswanathan, Mathuranathan: Amazon.sg: Books The blue box above shows what is actually sampled by the SDR, and the green box displays the portion of the spectrum we want. In order to avoid expensive RF front-end components, signal processing algorithms for compensating the IQ imbalance and DC offsets are a necessity. You may have encountered sampling without realizing it by recording audio with a microphone. A learner-friendly, practical and example driven book, Digital Modulations using Python gives you a solid background in building simulation models for digital modulation systems in Python version 3. The phase mismatch between the local oscillator outputs is captured by the parameter . Link to the color edition: https://www.amazon.com/dp/1712321633 .A learner-friendly, practical and example driven book, Digital Modulations using Python gives you a solid background in building simulation models for digital modulation systems in Python version 3. ax[1,0].plot(real(r2),imag(r2),'r. When we tune to a frequency with our SDR and receive samples, our information is stored in I and Q; this carrier does not show up in I and Q, assuming we tuned to the carrier. Once the parameters given in equation 6.5 and 6.6 are estimated during the preamble transmission, the IQ compensation during the normal data transmission is as follows 6.5 Visualizing the effect of receiver impairments 197 wi = di dq Pest .di p wq = 2 kest 1 Pest (6.7) Program 82: DigiCommPy\compensation.py: Compensation of IQ imbalance during data transmission class PilotEstComp(): def __init__(self,impObj): # constructor < see previous program > def pilot_est(self): < see previous program > def pilot_iqImb_compensation(self,d): """ Function to compensate IQ imbalance during the data transmission Parameters: d : The impaired received complex signal sequence Returns: w : IQ imbalance compensated complex signal sequence Usage: from compensation import PilotEstComp pltEstCompObj = PilotEstComp(impObj) #initialize pltEstCompObj.pilot_iqImb_compensation(d) #call function """ # estimate the Kest, Pest for the given model using pilot transmission self.pilot_est() d_dcRemoved = d - (mean(real(d)) + 1j* mean(imag(d))) I=real(d_dcRemoved); Q=imag(d_dcRemoved) wi= I; wq = (Q - self.Pest*I)/sqrt(1-self.Pest**2)/self.Kest return wi + 1j*wq 6.5 Visualizing the effect of receiver impairments With the receiver impairments model and compensation techniques in place, let us visualize their effects in a complex plane. In Figure 6.1, the power imbalance on the IQ branches is captured by the gain parameter g on the quadrature branch. The simplest PSK technique is called binary phase-shift keying (BPSK). The AWGN noise model and the code to compute theoretical symbol error rates are given in sections 4.1.2 and 4.1.3 respectively. These techniques are also called as Digital Modulation techniques.. Digital Modulation provides more information capacity, high data security, quicker system availability with great quality communication. He holds a Master's degree in Communication Engineering from BITS-Pilani, India. Although if you accidentally mix it up and assign Q to the cos() and I to the sin(), it wont make a difference for most situations. Available Features Channel Coding Consider how two waves that are 180 degrees out of phase are essentially the same wave with one multiplied by -1. When you take the FFT of a series of samples, it finds the frequency domain representation. The focus of this chapter is to introduce the reader to some of the basic models of receiver impairments like phase imbalances, gain imbalances and DC offsets. , ISBN-13 Please try again. You may also hear the term intermediate frequency (abbreviated as IF); for now, think of IF as an intermediate conversion step within a radio between baseband and bandpass/RF. The resulting optimized filter coefficients w[n] should match the impulse response h[n]. Digital Filter Design using Python for Power Engineering Applications: An Open Source Guide [1st ed.] Digital Modulations Using Python : (Color Edition) Item Length: 10in. import sounddevice as sd. ');ax[0,1].set_title('DC offsets only') ax[1,0].plot(real(s),imag(s),'b.') Because the SDR tunes to a center frequency, the 0 Hz portion of the FFT corresponds to the center frequency. This point, complex or not, is what this entire chapter has been building to, and we finally made it. 7. : The frequency at which we sample, i.e., the number of samples taken per second, is simply . Our LO will be set to 95 MHz because that is the frequency to which we ask the SDR to tune. I.e., we evaluate the analog signal at these intervals of . Downconversion (and upconversion) is done by a component called a mixer, usually represented in diagrams as a multiplication symbol inside a circle. We are sorry. : 1dda02c 18 minutes ago. Please try again. Universidad de Colima; Download full-text PDF Read full-text. AbeBooks.com: Digital Modulations using Python: (Color edition) (9781712321638) by Viswanathan, Mathuranathan and a great selection of similar New, Used and Collectible Books available now at great prices. Most of the time you see complex samples (IQ samples), you are at baseband. Our partners will collect data and use cookies for ad personalization and measurement. If there is a leak in the door then your microwave will jam WiFi signals and possibly also burn your skin. This downconversion happens before we sample. Digital modulation results if a new message is chosen each T s seconds (called the symbol interval) from a discrete set and mapped one-to-one into a signal parameter value (amplitude, phase, etc.). Lets say we sample at a rate Fs (samples shown in blue). For SDR-specific information about performing sampling, see one of the following chapters: For a discrete complex signal, i.e., one we have sampled, we can find the average power by taking the magnitude of each sample, squaring it, and then finding the mean: Remember that the absolute value of a complex number is just the magnitude, i.e.. You can also subtract out the mean from the samples in your window of observation, then take variance. What is plotted are the cosine, sine, and then the sum of the two. Tags communications, digital, modulation, demodulation, psk, qam Requires: Python >=3.6.4 Maintainers kirlf Classifiers. A learner-friendly, practical and example driven book, Digital Modulations using Python gives you a solid background in building simulation models for digital modulation systems in Python version 3. The symbol error rates are finally computed for following four cases: (a) received signal (with no compensation), (b) with DC compensation alone, (c) with DC compensation and blind IQ compensation and (d) with DC compensation and pilot based IQ compensation. It indicates that our sample rate was too low because the same samples could have come from two different functions, leading to ambiguity. Digital Modulations using Python (Color edition) - Desertcart Using the python programming language, you will learn how to decode information from a sinusoid's varying frequency using a process called fm demodulation. For instance, consider a higher order modulation like 64-QAM. 1.1 Trading off simplicity and bandwidth 1.2 Industry trends 2. Frequency modulation Python matplotlib visualisation Raw frequency_modulation_visualisation.py This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. 158-164. ${cardName} not available for the seller you chose. Reviewed in the United States on April 27, 2020. pip install ModulationPy Try again. 6581, May 1996 2. Copy PIP instructions, View statistics for this project via Libraries.io, or by using our public dataset on Google BigQuery, Tags ${cardName} unavailable for quantities greater than ${maxQuantity}. Another option is to change the frequency of the carrier, i.e., shift it slightly up or down, which is what FM radio does. The utility of this behavior is that we can control the phase and amplitude of a resulting sine wave by adjusting the amplitudes I and Q (we dont have to adjust the phase of the cosine or sine). C.R Johnson, Hr. The term quadrature has many meanings, but in the context of DSP and SDR it refers to two waves that are 90 degrees out of phase. PSK is of two types, depending upon the . IQ sampling is more easily understood by using the transmitters point of view, i.e., considering the task of transmitting a RF signal through the air. AbeBooks.com: Digital Modulations using Python: (Black & White edition) (9781712342749) by Viswanathan, Mathuranathan and a great selection of similar New, Used and Collectible Books available now at great prices. : Hence, digital modulation techniques have a greater demand, for their capacity to convey larger amounts of . Take the FFT of our samples. Link to the color edition: https:. It involves downconversion but not all the way to 0 Hz. In Python, calculating the average power will look like: Here is a very useful trick for calculating the average power of a sampled signal. Fast and free shipping free returns cash on delivery available on eligible purchase. Meng W. Namgoong, Direct-conversion RF receiver design, IEEE Transaction on Communications, 49(3):518529, March 2001 2. to see options Walmart.com Free 90-Day returns Add to list Add to registry Starting from $47.52 Compare all sellers Get free delivery, shipping and more* *Restrictions apply Start 30-day free trial About this item Product details Digital Modulation In this chapter we will discuss actually transmitting data using digital modulation and wireless symbols! Link to the black & white edition: https:. The implemented simulation models shown in this book, provide . In other words, the Nyquist Rate is the minimum rate at which a (finite bandwidth) signal needs to be sampled to retain all of its information. all systems operational. Help others learn more about this product by uploading a video! z z Fig. The presentation is geared for practicing enginee LO leakage is additional energy created through the combination of frequencies. A DC offset is a common artifact in direct conversion receivers, which is the architecture used for SDRs like the PlutoSDR, RTL-SDR, LimeSDR, and many Ettus USRPs. We go from sending to , meaning our carrier shifts phase by 90 degrees when we switch from one sample to another. Go to file. Please try again later. Be the first to receive exclusive offers and the latest news on our products and services directly in your inbox. It looks like WhatsApp is not installed on your phone. It presents the key topics with required theoretical background along with the implementation details in the form of Python scripts.Key topics: Basics of signal processing, essential for implementing digital modulation techniques - generation of test signals, interpreting FFT results, power and energy of a signal, methods to compute convolution . A learner-friendly, practical and example driven book, Digital Modulations using Python gives you a solid background in building simulation models for digital modulation systems in Python version 3. v u L u 2 u u zq [k] u k=1 (6.5) Kest = u L u t z2 [k] i k=1 L (zi [k].zq [k]) Pest = k=1 (6.6) L k=1 z2i [k] 196 6 Receiver Impairments and Compensation where, the complex signal z = zi + jzq , represents the impaired version of the long preamble as given in the IEEE 802.11a specification [6]. Access codes and supplements are not guaranteed with used items. You may have seen complex numbers before in other classes. Also, the phase shifts as we slowly remove or add one of the two parts. : Program 81: DigiCommPy\compensation.py: IQ imbalance estimation using Pilot transmission class PilotEstComp(): # Class: PilotEstComp (Pilot based estimation and compensation) # Attribute definitions: # self.impObj: reference to the object implementing the impairment model # self.Kest : estimated gain imbalance # self.Pest : estimated phase mismatch # self.preamble : time domain representation long preamble (IEEE 802.11a) def __init__(self,impObj): # constructor self.impObj = impObj self.Kest = 1 self.Pest = 0 # Length 64 - long preamble in IEEE 802.11a (frequency domain representation) preamble_freqDom = np.array([0,0,0,0,0,0,1,1,\ -1,-1,1,1,-1,1,-1,1,\ 1,1,1,1,1,-1,-1,1,1,\ -1,1,-1,1,1,1,1,0,1,\ -1,-1,1,1,-1,1,-1,1,\ -1,-1,-1,-1,-1,1,1,\ -1,-1,1,-1,1,-1,1,1,\ 1,1,0,0,0,0,0]) from scipy.fftpack import ifft self.preamble=ifft(preamble_freqDom,n = 64) def pilot_est(self): """ IQ imbalance estimation using Pilot transmission Computes: Kest - estimated gain imbalance Pest - estimated phase mismatch """ # send known preamble through the impairments model r_preamb = self.impObj.receiver_impairments(self.preamble) # remove DC imbalance before IQ imbalance estimation z_preamb= r_preamb - (mean(real(r_preamb)) + 1j* mean(imag(r_preamb))) # IQ imbalance estimation I=real(z_preamb); Q=imag(z_preamb) self.Kest = sqrt(sum((Q*Q))/sum(I*I)) # estimated gain imbalance self.Pest = sum(I*Q)/sum(I*I) # estimated phase mismatch def pilot_iqImb_compensation(self,d): < see next program > Let d = di + jdq be the impaired version of complex signal received during the normal data transmission interval. Conversely, bandpass refers to when a signal exists at some RF frequency nowhere near 0 Hz, that has been shifted up for the purpose of wireless transmission. In reality, analog components in the RF front-end are not perfect and hence it is impossible to satisfy these requirements. We tell the SDR what frequency we want to sample at, or what frequency we want to transmit our samples at. For a given complex number where is the real part and is the imaginary part: In Python you can use np.abs(x) and np.angle(x) for the magnitude and phase. And its much easier to adjust two amplitudes and perform an addition operation compared to adjusting an amplitude and a phase. The result is that our transmitter will look something like this: We only need to generate one sine wave and shift it by 90 degrees to get the Q portion. To accurately sample any given signal, the sample rate must be at least twice the frequency of the maximum frequency component. As an example, lets say we want to view 5 MHz of spectrum at 100 MHz. The adaptive filter should be able to identify the response of a short FIR filter whose impulse response is known prior to the test. Full content visible, double tap to read brief content. Digital Modulations using Python: (Color edition), This paperback is a color edition. 1. The code re-uses many functions defined in chapter 3 and chapter 4. The unimpaired sequence and the sequence affected by receiver impairments are plotted on a complex plane as shown in Figure 6.4. To run this sample, get started with a free trial of PDFTron SDK. For those who prefer to see the math; let represent sample , usually an integer starting at 0. 6.5: Simulation model for M-QAM system with receiver impairments Fig. The output of an FFT is an array of complex numbers, and each complex number gives you the magnitude and phase, and the index of that number gives you the frequency. import time. Please try enabling it if you encounter problems. As a simple example, lets say we transmit the IQ sample 1+0j, and then we switch to transmitting 0+1j. Donate today! 6.1 Introduction Direct conversion image rejection receivers [1] for IQ processing - are quite popular in modern RF receiver front-end designs. Digital data, digital signal - Equipment less complex and expensive than digital -to-analog modulation equipment Analog data, digital signal - Permits use of modern digital transmission and switching equipme nt Digital data, analog signal - Some transmission media will only propagate analog signals - E.g., unguided media (air) No Cost EMI available EMI options Save Extra with 3 offers Figure 6.5 shows the functional blocks used in this performance simulation. We dont actually have to generate a sine wave, shift by 90, multiply or addthe SDR does that for us. The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. I have done FFT's for over a decade and even written a open source library, yet I learned a few things I did not fully understand by reading his book. Each symbol represents n bits of information where n = log 2m bits/symbol. Another form of electromagnetic waves is light. Program 79: DigiCommPy\impairments.py: IQ imbalance model class ImpairmentModel(): def __init__(self,g=1,phi=0,dc_i=0,dc_q=0): # constructor < see section 6.1 for details > def receiver_impairments(self,r): < see section 6.1 for details > def __iq_imbalance(self,r): #private member function """ Add IQ imbalance impairments in a complex baseband.