Rician flat-fading channel – simulation

In wireless environments, transmitted signal may be subjected to multiple scatterings before arriving at the receiver. This gives rise to random fluctuations in the received signal and this phenomenon is called fading. The scattered version of the signal is designated as non line of sight (NLOS) component. If the number of NLOS components are sufficiently large, the fading process is approximated as the sum of large number of complex Gaussian process whose probability-density-function follows Rayleigh distribution.

Rayleigh distribution is well suited for the absence of a dominant line of sight (LOS) path between the transmitter and the receiver. If a line of sight path do exist, the envelope distribution is no longer Rayleigh, but Rician (or Ricean). If there exists a dominant LOS component, the fading process can be represented as the sum of complex exponential and a narrowband complex Gaussian process g(t). If the LOS component arrive at the receiver at an angle of arrival (AoA) θ, phase ɸ and with the maximum Doppler frequency fD, the fading process in baseband can be represented as (refer [1])

\[h(t)= \underbrace{\sqrt{\frac{K \Omega}{K +1}}}_\text{A:=} e^{\left( j2 \pi f_D cos(\theta)t+\phi \right)} + \underbrace{\sqrt{\frac{\Omega}{K+1}}}_\text{S:=}g(t)\]

where, K represents the Rician K factor given as the ratio of power of the LOS component A2 to the power of the scattered components (S2) marked in the equation above.

\[K=\frac{A^2}{S^2}\]

The received signal power Ω is the sum of power in LOS component and the power in scattered components, given as Ω=A2+S2. The above mentioned fading process is called Rician fading process. The best and worst-case Rician fading channels are associated with K=∞ and K=0 respectively. A Ricean fading channel with K=∞ is a Gaussian channel with a strong LOS path. Ricean channel with K=0 represents a Rayleigh channel with no LOS path.

The statistical model for generating flat-fading Rician samples is discussed in detail in chapter 11 section 11.3.1 in the book Wireless communication systems in Matlab (see the related article here). With respect to the simulation model shown in Figure 1(b), given a K factor, the samples for the Rician flat-fading samples are drawn from the following random variable

\[h= | X + jY |\]

where X,Y ~ N(μ,σ2) are Gaussian random variables with non-zero mean μ and standard deviation σ as given in references [2] and [3].

\[\mu = g_1 =\sqrt{\frac{K}{2\left(K+1\right)}} \quad \quad \sigma = g_2 = \sqrt{\frac{1}{2\left(K+1\right)}}\]

Kindly refer the book Wireless communication systems in Matlab for the script on generating channel samples for Ricean flat-fading.

Simulation model for modulation and detection over flat fading channel
Figure 1: Simulation model for modulation and detection over flat fading channel

Simulation and performance results

In chapter 5 of the book Wireless communication systems in Matlab, the code implementation for complex baseband models for various digital modulators and demodulator are given. The computation and generation of AWGN noise is also given in the book. Using these models, we can create a unified simulation for code for simulating the performance of various modulation techniques over Rician flat-fading channel the simulation model shown in Figure 1(b).

An unified approach is employed to simulate the performance of any of the given modulation technique – MPSK, MQAM or MPAM. The simulation code (given in the book) will automatically choose the selected modulation type, performs Monte Carlo simulation, computes symbol error rates and plots them against the theoretical symbol error rate curves. The simulated performance results obtained for various modulations are shown in the Figure 2.

Figure 2: Performance of various modulations over Ricean flat fading channel

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References

[1] C. Tepedelenlioglu, A. Abdi, and G. B. Giannakis, The Ricean K factor: Estimation and performance analysis, IEEE Trans. Wireless Communication ,vol. 2, no. 4, pp. 799–810, Jul. 2003.↗
[2] R. F. Lopes, I. Glover, M. P. Sousa, W. T. A. Lopes, and M. S. de Alencar, A simulation framework for spectrum sensing, 13th International Symposium on Wireless Personal Multimedia Communications (WPMC 2010), Out. 2010.
[3] M. C. Jeruchim, P. Balaban, and K. S. Shanmugan, Simulation of Communication Systems, Methodology, Modeling, and Techniques, second edition Kluwer Academic Publishers, 2000.↗

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Constellation diagram – investigate phase transitions

The phase transition properties of the different variants of QPSK schemes and MSK, are easily investigated using constellation diagram. Let’s demonstrate how to plot the signal space constellations, for the various modulations used in the transmitter.

Typically, in practical applications, the baseband modulated waveforms are passed through a pulse shaping filter for combating the phenomenon of intersymbol interference (ISI). The goal is to plot the constellation plots of various pulse-shaped baseband waveforms of the QPSK, O-QPSK and π/4-DQPSK schemes. A variety of pulse shaping filters are available and raised cosine filter is specifically chosen for this demo. The raised cosine (RC) pulse comes with an adjustable transition band roll-off parameter α, using which the decay of the transition band can be controlled.

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The RC pulse shaping function is expressed in frequency domain as

Equivalently, in time domain, the impulse response corresponds to

A simple evaluation of the equation (2) produces singularities (undefined points) at p(t = 0) and p(t = ±Tsym/(2α)). The value of the raised cosine pulse at these singularities can be obtained by applying L’Hospital’s rule [1] and the values are

Using the equations above, the raised cosine filter is implemented as a function (refer the books Digital Modulations using Python and Digital Modulations using Matlab for the code).

The function is then tested. It generates a raised cosine pulse for the given symbol duration Tsym = 1s and plots the time-domain view and the frequency response as shown in Figure 1. From the plot, it can be observed that the RC pulse falls off at the rate of 1/|t|3 as t→∞, which is a significant improvement when compared to the decay rate of a sinc pulse which is 1/|t|. It satisfies Nyquist criterion for zero ISI – the pulse hits zero crossings at desired sampling instants. The transition bands in the frequency domain can be made gradual (by controlling α) when compared to that of a sinc pulse.

Figure 1: Raised-cosine pulse and its manifestation in frequency domain

Plotting constellation diagram

Now that we have constructed a function for raised cosine pulse shaping filter, the next step is to generate modulated waveforms (using QPSK, O-QPSK and π/4-DQPSK schemes), pass them through a raised cosine filter having a roll-off factor, say α = 0.3 and finally plot the constellation. The constellation for MSK modulated waveform is also plotted.

Figure 2: Constellations plots for: (a) a = 0.3 RC-filtered QPSK, (b) α = 0.3 RC-filtered O-QPSK, (c) α = 0.3 RC-filtered π/4-DQPSK and (d) MSK

Conclusions

The resulting simulated plot is shown in the Figure 2. From the resulting constellation diagram, following conclusions can be reached.

  • Conventional QPSK has 180° phase transitions and hence it requires linear amplifiers with high Q factor
  • The phase transitions of Offset-QPSK are limited to 90° (the 180° phase transitions are eliminated)
  • The signaling points for π/4-DQPSK is toggled between two sets of QPSK constellations that are shifted by 45° with respect to each other. Both the 90° and 180° phase transitions are absent in this constellation. Therefore, this scheme produces the lower envelope variations than the rest of the two QPSK schemes.
  • MSK is a continuous phase modulation, therefore no abrupt phase transition occurs when a symbol changes. This is indicated by the smooth circle in the constellation plot. Hence, a band-limited MSK signal will not suffer any envelope variation, whereas, the rest of the QPSK schemes suffer varied levels of envelope variations, when they are band-limited.

References

[1] Clay S. Turner, Raised Cosine and Root Raised Cosine Formulae, Wireless Systems Engineering, Inc, (May 29, 2007) V1.2↗

In this chapter

Digital Modulators and Demodulators - Passband Simulation Models
Introduction
Binary Phase Shift Keying (BPSK)
 □ BPSK transmitter
 □ BPSK receiver
 □ End-to-end simulation
Coherent detection of Differentially Encoded BPSK (DEBPSK)
● Differential BPSK (D-BPSK)
 □ Sub-optimum receiver for DBPSK
 □ Optimum noncoherent receiver for DBPSK
Quadrature Phase Shift Keying (QPSK)
 □ QPSK transmitter
 □ QPSK receiver
 □ Performance simulation over AWGN
● Offset QPSK (O-QPSK)
● π/p=4-DQPSK
● Continuous Phase Modulation (CPM)
 □ Motivation behind CPM
 □ Continuous Phase Frequency Shift Keying (CPFSK) modulation
 □ Minimum Shift Keying (MSK)
Investigating phase transition properties
● Power Spectral Density (PSD) plots
Gaussian Minimum Shift Keying (GMSK)
 □ Pre-modulation Gaussian Low Pass Filter
 □ Quadrature implementation of GMSK modulator
 □ GMSK spectra
 □ GMSK demodulator
 □ Performance
● Frequency Shift Keying (FSK)
 □ Binary-FSK (BFSK)
 □ Orthogonality condition for non-coherent BFSK detection
 □ Orthogonality condition for coherent BFSK
 □ Modulator
 □ Coherent Demodulator
 □ Non-coherent Demodulator
 □ Performance simulation
 □ Power spectral density

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QPSK – Quadrature Phase Shift Keying

Quadrature Phase Shift Keying (QPSK) is a form of phase modulation technique, in which two information bits (combined as one symbol) are modulated at once, selecting one of the four possible carrier phase shift states.

Figure 1: Waveform simulation model for QPSK modulation

The QPSK signal within a symbol duration \(T_{sym}\) is defined as

\[s(t) = A \cdot cos \left[2 \pi f_c t + \theta_n \right], \quad \quad 0 \leq t \leq T_{sym},\; n=1,2,3,4 \quad \quad (1) \]

where the signal phase is given by

\[\theta_n = \left(2n – 1 \right) \frac{\pi}{4} \quad \quad (2)\]

Therefore, the four possible initial signal phases are \(\pi/4, 3 \pi/4, 5 \pi/4\) and \(7 \pi/4\) radians. Equation (1) can be re-written as

\[\begin{align} s(t) &= A \cdot cos \theta_n \cdot cos \left( 2 \pi f_c t\right) – A \cdot sin \theta_n \cdot sin \left( 2 \pi f_c t\right) \\ &= s_{ni} \phi_i(t) + s_{nq} \phi_q(t) \quad\quad \quad \quad \quad\quad \quad \quad \quad\quad \quad \quad \quad\quad \quad \quad (3) \end{align} \]

The above expression indicates the use of two orthonormal basis functions: \( \left\langle \phi_i(t),\phi_q(t)\right\rangle\) together with the inphase and quadrature signaling points: \( \left\langle s_{ni}, s_{nq}\right\rangle\). Therefore, on a two dimensional co-ordinate system with the axes set to \( \phi_i(t)\) and \(\phi_q(t)\), the QPSK signal is represented by four constellation points dictated by the vectors \(\left\langle s_{ni}, s_{nq}\right\rangle\) with \( n=1,2,3,4\).

This article is part of the following books
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Digital Modulations using Python ISBN: 978-1712321638
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The transmitter

The QPSK transmitter, shown in Figure 1, is implemented as a matlab function qpsk_mod. 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.

Refer Digital Modulations using Matlab : Build Simulation Models from Scratch for full Matlab code.
Refer Digital Modulations using Python for full Python code

File 1: qpsk_mod.m: QPSK modulator

function [s,t,I,Q] = qpsk_mod(a,fc,OF)
%Modulate an incoming binary stream using conventional QPSK
%a - input binary data stream (0's and 1's) to modulate
%fc - carrier frequency in Hertz
%OF - oversampling factor (multiples of fc) - at least 4 is better
%s - QPSK modulated signal with carrier
%t - time base for the carrier modulated signal
%I - baseband I channel waveform (no carrier)
%Q - baseband Q channel waveform (no carrier)
L = 2*OF;%samples in each symbol (QPSK has 2 bits in each symbol)
ak = 2*a-1; %NRZ encoding 0-> -1, 1->+1
I = ak(1:2:end);Q = ak(2:2:end);%even and odd bit streams
I=repmat(I,1,L).'; Q=repmat(Q,1,L).';%even/odd streams at 1/2Tb baud
I = I(:).'; Q = Q(:).'; %serialize
fs = OF*fc; %sampling frequency
t=0:1/fs:(length(I)-1)/fs; %time base
iChannel = I.*cos(2*pi*fc*t);qChannel = -Q.*sin(2*pi*fc*t);
s = iChannel + qChannel; %QPSK modulated baseband signal

The timing diagram for BPSK and QPSK modulation is shown in Figure 2. For BPSK modulation the symbol duration for each bit is same as bit duration, but for QPSK the symbol duration is twice the bit duration: \(T_{sym}=2T_b\). Therefore, if the QPSK symbols were transmitted at same rate as BPSK, it is clear that QPSK sends twice as much data as BPSK does. 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 multiplied by \(cos (2 \pi f_c t)\) and the signal on the quadrature arm is multiplied by \(-sin (2 \pi f_c t)\). QPSK modulated signal is obtained by adding the signal from both in-phase and quadrature arms.

Note: The oversampling rate for the simulation is chosen as \(L=2 f_s/f_c\), where \(f_c\) is the given carrier frequency and \(f_s\) is the sampling frequency satisfying Nyquist sampling theorem with respect to the carrier frequency (\(f_s \geq f_c\)). This configuration gives integral number of carrier cycles for one symbol duration.

Figure 2: Timing diagram for BPSK and QPSK modulations

The receiver

Due to its special relationship with BPSK, the QPSK receiver takes the simplest form as shown in Figure 3. In this implementation, the I-channel and Q-channel signals are individually demodulated in the same way as that of BPSK demodulation. After demodulation, the I-channel bits and Q-channel sequences are combined into a single sequence. The function qpsk_demod implements a QPSK demodulator as per Figure 3.

Read more about QPSK, implementation of their modulator and demodulator, performance simulation in these books:

Figure 3: Waveform simulation model for QPSK demodulation

Performance simulation over AWGN

The complete waveform simulation for the aforementioned QPSK modulation and demodulation is given next. The simulation involves, generating random message bits, modulating them using QPSK modulation, addition of AWGN channel noise corresponding to the given signal-to-noise ratio and demodulating the noisy signal using a coherent QPSK receiver. The waveforms at the various stages of the modulator are shown in the Figure 4.

Figure 4: Simulated QPSK waveforms at the transmitter side

The performance simulation for the QPSK transmitter-receiver combination was also coded in the code given above and the resulting bit-error rate performance curve will be same as that of conventional BPSK. A QPSK signal essentially combines two orthogonally modulated BPSK signals. Therefore, the resulting performance curves for QPSK – \(E_b/N_0\) Vs. bits-in-error – will be same as that of conventional BPSK.

QPSK variants

QPSK modulation has several variants, three such flavors among them are: Offset QPSK, π/4-QPSK and π/4-DQPSK.

Offset-QPSK

Offset-QPSK is essentially same as QPSK, except that the orthogonal carrier signals on the I-channel and the Q-channel are staggered (one of them is delayed in time). In OQPSK, the orthogonal components cannot change states at the same time, this is because the components change state only at the middle of the symbol periods (due to the half symbol offset in the Q-channel). This eliminates 180° phase shifts all together and the phase changes are limited to 0° or 90° every bit period.

Elimination of 180° phase shifts in OQPSK offers many advantages over QPSK. Unlike QPSK, the spectrum of OQPSK remains unchanged when band-limited [1]. Additionally, OQPSK performs better than QPSK when subjected to phase jitters [2]. Further improvements to OQPSK can be obtained if the phase transitions are avoided altogether – as evident from continuous modulation schemes like Minimum Shift Keying (MSK) technique.

π/4-QPSK and π/4-DQPSK

In π/4-QPSK, the signaling points of the modulated signals are chosen from two QPSK constellations that are just shifted π/4 radians (45°) with respect to each other. Switching between the two constellations every successive bit ensures that the phase changes are confined to odd multiples of 45°. Therefore, phase transitions of 90° and 180° are eliminated.

π/4-QPSK preserves the constant envelope property better than QPSK and OQPSK. Unlike QPSK and OQPSK schemes, π/4-QPSK can be differentially encoded, therefore enabling the use of both coherent and non-coherent demodulation techniques. Choice of non-coherent demodulation results in simpler receiver design. Differentially encoded π/4-QPSK is referred as π/4-DQPSK.

Read more about QPSK and its variants, implementation of their modulator and demodulator, performance simulation in these books:

Constellation diagram

The phase transition properties of the different variants of QPSK schemes, are easily investigated using constellation diagram. Refer this article that discusses the method to plot signal space constellations, for the various modulations used in the transmitter.

Refer Digital Modulations using Matlab : Build Simulation Models from Scratch for full Matlab code.
Refer Digital Modulations using Python for full Python code

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References

[1] S. A. Rhodes, “Effects of hardlimiting on bandlimited transmissions with conventional and offset QPSK modulation”, in Proc. Nat. TeIecommun. Conf., Houston, TX, 1972, PP. 20F/1-20F/7
[2] S. A. Rhodes, “Effect of noisy phase reference on coherent detection of offset QPSK signals”, IEEE Trans. Commun., vol. COM-22, PP. 1046-1055, Aug. 1974.↗

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Topics in this chapter

Digital Modulators and Demodulators - Passband Simulation Models
Introduction
Binary Phase Shift Keying (BPSK)
 □ BPSK transmitter
 □ BPSK receiver
 □ End-to-end simulation
Coherent detection of Differentially Encoded BPSK (DEBPSK)
● Differential BPSK (D-BPSK)
 □ Sub-optimum receiver for DBPSK
 □ Optimum noncoherent receiver for DBPSK
Quadrature Phase Shift Keying (QPSK)
 □ QPSK transmitter
 □ QPSK receiver
 □ Performance simulation over AWGN
● Offset QPSK (O-QPSK)
● π/p=4-DQPSK
● Continuous Phase Modulation (CPM)
 □ Motivation behind CPM
 □ Continuous Phase Frequency Shift Keying (CPFSK) modulation
 □ Minimum Shift Keying (MSK)
Investigating phase transition properties
● Power Spectral Density (PSD) plots
Gaussian Minimum Shift Keying (GMSK)
 □ Pre-modulation Gaussian Low Pass Filter
 □ Quadrature implementation of GMSK modulator
 □ GMSK spectra
 □ GMSK demodulator
 □ Performance
● Frequency Shift Keying (FSK)
 □ Binary-FSK (BFSK)
 □ Orthogonality condition for non-coherent BFSK detection
 □ Orthogonality condition for coherent BFSK
 □ Modulator
 □ Coherent Demodulator
 □ Non-coherent Demodulator
 □ Performance simulation
 □ Power spectral density