Oqpsk demodulation matlab torrent

oqpsk demodulation matlab torrent

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QPSK Demodulation Using Matched Filter on Matlab

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I think this might be due to that there are no transition through the origin. The origin of coarse is a point that is phase invariant. For QPSK a carrier offset causes a spinning constellation but the transitions would still be clearly visible as they still may pass through the origin in time with the symbols. Delaying one arm so it becomes a QPSK signal again did not seem to work either. I think this might have been due to the cross talk between the real and imaginary channels when the rotation happens.

They say that there are only a few such phase invariant algorithms that have been proposed for OQPSK. So I was quite lucky to find the algorithm. I have no idea how the algorithm works but it is a very simple algorithm and their picture of a block diagram of the algorithm was all I needed to implement it. The algorithm only produces an error signal. I added a few bits and pieces so the error signal can then adjust the symbol timing.

Adding everything, the following figure shows my complete implementation from audio until symbol clock output. The output of their error signal algorithm is eta, and a frequency domain of the signal is shown in the following figure. A very loud oscillation can be seen that has the same frequency and is in phase with the symbol timing of the transmitter. I am really impressed with this algorithm.

Using this algorithm means being able to obtain symbol timing information without any information of the carrier phase. To track the carrier one first needs to at least roughly estimate the frequency of the signal. Simply squaring the audio signal and taking the FFT results in the following figure. As can be seen there is no peak where the carrier is. However, there are two peaks evenly spaced around where the carrier would be.

Taking the average of the two peaks you obtain the carrier frequency of the transmission. Initially I tried using the trick whereby you raise the complex symbol point to the power of however many points there are in the constellation. Sampling at optimal time produces an eight point constellation so raising the point to the eighth power was needed. This ends up producing a point that is invariant with respect to the sent symbol and can hence be used to adjust the rotation of the constellation which is what carrier tracking is.

The results of doing this were not good for low SNR signal-to-noise ratio values. This can be seen in the following figure. Another option was needed to bring the demodulation performance closer to that of an ideal demodulator. Simon shows in figure 1 of his article supposedly a block diagram of these two equations. However, from my understanding of his block diagram it does not correctly implement equations 3 and 4 of his article.

Therefore, I converted his two equations myself into what I believe to be a correct block diagram of the two equations. Some parts of the block diagram such as the funny looking double lines going into the mixer are the complex numbers being split into the real and imaginary components while tanh means the tanh function is being performed on the signal that travels down that line.

Disconnecting the loop filter and looking at the frequency response of the error output for a 3. As can be seen this is a nice looking graph. The noise drops as one moves closer to 0 Hz and this is precisely where most offsets will occur.

This transformation is not necessary. I will show you why it is so. The obvious choice for implementation seems to be the atan function in Matlab. However, usage of. Refer section for the mathematical details of Toeplitz matrix and its relationship to convolution operation.

It makes uses of the function convmatrix. Note that FFT is a direct implementation of circular convolution in time domain. Here,we are attempting to compute linear convolution using circular convolution or FFT with zero-padding either one of the input sequence.

This causes inefficiency when compared to circular convolution. Nevertheless, this method still provides O N log 2 N savings over brute-force method. The carrier signal has the following parameters A - amplitude of the carrier. The phase modulated signal shown in equation 1. A phase modulated signal of form x t can be demodulated by forming an analytic signal by applying hilbert transform and then extracting the instantaneous phase.

This linear offset needs to be subtracted from the instantaneous phase to obtain the information bearing modulated signal. If the carrier frequency is known at the receiver, this can be done easily. The following Matlab code demonstrates all these methods. The resulting plots are shown in Figures 1. The passband model is also called waveform level simulation model.

The waveform level simulation techniques, described in this chapter, are used to represent the physical interactions of the transmitted signal with the channel. In the waveform level simulations, the transmitted signal, the noise and received signal are all represented by samples of waveforms. Typically, a waveform level simulation uses many samples per symbol.

For the computation of error rate performance of various digital modulation techniques, the value of the symbol at the symbol-sampling time instant is all the more important than the look of the entire waveform. In such a case, the detailed waveform level simulation is not required, instead equivalent baseband discrete-time model, described in chapter 3 can be used.

Discrete-time equivalent channel model requires only one sample per symbol, hence it consumes less memory and yields results in a very short span of time. In any communication system, the transmitter operates by modulating the information bearing baseband waveform on to a sinusoidal RF carrier resulting in a passband signal. The carrier frequency, chosen for transmission, varies for different applications. For example, FM radio uses MHz carrier frequency range, whereas for indoor wireless networks the center frequency of transmission is 1.

Hence, the carrier frequency is not the component that contains the information, rather it is the baseband signal that contains the information that is being conveyed. Actual RF transmission begins by converting the baseband signals to passband signals by the process of up-conversion.

Similarly, the passband signals are down-converted to baseband at the receiver, before actual demodulation could begin. Based on this context, two basic types of behavioral models exist for simulation of communication systems - passband models and its baseband equivalent. In the passband model, every cycle of the RF carrier is simulated in detail and the power spectrum will be concentrated near the carrier frequency f c.

Hence, passband models consume more memory, as every point in the RF carrier needs to be stored in computer memory for simulation. On the other hand, the signals in baseband models are centered near zero frequency. In baseband equivalent models, the RF carrier is suppressed and therefore the number of samples required for simulation is greatly reduced. Furthermore, if the behavior of the system is well understood, the baseband model can be further In digital modulation techniques, a set of basis functions are chosen for a particular modulation scheme.

Generally, the basis functions are orthogonal to each other. Basis functions can be derived using Gram Schmidt orthogonalization procedure [1]. Once the basis functions are chosen, any vector in the signal space can be represented as a linear combination of them. In BPSK, only one sinusoid is taken as the basis function.

Modulation is achieved by varying the phase of the sinusoid depending on the message bits. It has no projection on the y axis quadrature. This means that the BPSK modulated signal will have an in-phase component but no quadrature component.

This is because it has only one basis function. It can be noted that the carrier phases are 18 apart and it has constant envelope. Equation 2. In this implementation, a splitter separates the odd and even bits from the generated information bits.

Each stream of odd bits quadrature arm and even bits in-phase arm are converted to NRZ format in a parallel manner. After oversampling and pulse shaping, it is intuitively clear that the signal on the I-arm and Q-arm are BPSK signals with symbol duration 2T b. The signal on the in-phase arm is then.

QPSK modulated signal is obtained by adding the signal from both in-phase and quadrature arms. This configuration gives integral number of carrier cycles for one symbol duration. Program 2. MSK modulation provides all the desired qualities loved by the communication engineers - it provides constant envelope, a very compact spectrum compared to QPSK and OQPSK, and a good error rate performance. The phase trajectory of MSK in Figure 2. The receiver can exploit these phase transitions without any ambiguity and it can provide better error rate performance.

This is the main motivation behind the MSK technique. Therefore, the half-cycle cosine and sine functions are offset from each other by T b seconds. This offset relationship between the inphase and quadrature components is more similar to that of a OQPSK signal construct [6]. Figure 2. However, the half-cycle functions in the MSK are not simple reshaping waveforms.

It has features such as constant envelope, compact spectrum and good error rate performance. The fundamental problem with MSK is that the spectrum is not compact enough to satisfy the stringent requirements with respect to out-ofband radiation for technologies like GSM and DECT standard.

These technologies have very high data rates approaching the RF channel bandwidth. A plot of MSK spectrum Figure 2. This is problematic, since it causes severe out-of-band interference in systems with closely spaced adjacent channels.

To satisfy such requirements, the MSK spectrum can be easily manipulated by using a pre-modulation low pass filter LPF. Sharp cut-off and narrow bandwidth - needed to suppress high frequency components. Lower overshoot in the impulse response - providing protection against excessive instantaneous frequency deviations. Effectively, a variable parameter called BT b, the product of 3-dB bandwidth of the LPF and the desired data-rate T b, is often used by the designers to control the amount of spectrum efficiency required for the desired application.

The Gaussian impulse response is of infinite duration and hence in digital implementations it has to be defined for a finite interval, as dictated by the function argument k in the code shown next. For conventional designs based on CPM representation, refer [11].

Quadrature design is another implementation for GMSK modulator that can be easily realized in software. Explain Digital communication system with a neat block diagram. What are the differences between digital and analog communication systems?

Design Goals 2. Error Probability Plane 3. Nyquist Minimum Bandwidth 4. Shannon Hartley Capacity Theorem 5. Bandwidth Efficiency Plane 6. Modulation and. Digital Communication System Purpose: communicate information at required rate between geographically separated locations reliably quality Important point: rate, quality spectral bandwidth, power requirements.

Outline Introduction Signal, random variable, random process and spectra Analog modulation Analog to digital conversion Digital transmission through baseband channels Signal space representation Optimal. What is the. Digital Communication System Purpose: communicate information at certain rate between geographically separated locations reliably quality Important point: rate, quality spectral bandwidth requirement.

Digital Modulation Schemes 1. In binary data transmission DPSK is preferred to PSK because a a coherent carrier is not required to be generated at the receiver b for a given energy per bit, the probability. Digital Modulation Revision Professor Richard Harris Objectives To identify the key points from the lecture material presented in the Digital Modulation section of this paper.

What is in the examination. Digital modulation formats:. Specifically, we review some basic. What is the purpose of sample and hold circuit 2. What is the difference between natural sampling. Chapter 6. Rappaport, Wireless Communications - Principles and Practice,. Detection and Estimation of Signals in Noise Dr. Lecture 2 General concepts Digital modulation in general Optical modulation Direct modulation External modulation Modulation formats Differential detection Coherent detection Fiber Optical Communication.

Three hours plus 10 minutes reading time. Total Number of Questions:. Time Reversal. Synchronized Averaging covered in lecture 1 2. Moving Average Filters today s topic 3. Draw the block diagram of basic digital communication system.

How it is different from analog communication system. What are the advantages of. Good news: No complicated mathematics and calculations! Concepts: Understanding and remember! Homework: review. Zahid A. Draw functional block diagram of DCS and explain the significance of. In Soo Ahn Dr. Thomas L.

Mathematical models for communication channels David Johns johns eecg. Time: 1 Hour Class: T. Marks: 30 Q. Assume the audio signal B. I Find Nyquist rate. II If the Nyquist. Lecture 9: Spread Spectrum Modulation Techniques Spread spectrum SS modulation techniques employ a transmission bandwidth which is several orders of magnitude greater than the minimum required bandwidth. McNames Keep your exam flat during the entire exam. If you have to leave the exam temporarily,.

To be able. Mathys Problem Set 1 Solutions are due Mon. Modulation Modulation is a way to vary the amplitude and phase of a sinusoidal carrier waveform in order to transmit information. Orthonormal Representation of Signals Introduction An analogue communication system is designed for the transmission of information in analogue form.

In practice,. Chapter 3 Communication Concepts 1 Sections to be covered 3. Chapter 2 Direct-Sequence Systems A spread-spectrum signal is one with an extra modulation that expands the signal bandwidth greatly beyond what is required by the underlying coded-data modulation.

In this laboratory you will.

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QPSK Quadrature Phase Shift Keying (Basics, Modulator, Waveforms, Demodulator \u0026 Applications)

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