Mat lab for bplc
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The document describes using MATLAB to simulate and analyze the transfer function of a powerline channel between two access points. It obtains the attenuation in signal strength versus frequency and phase distortion versus frequency. It then calculates the impulse response of the powerline channel by taking the inverse Fourier transform of the transfer function.
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![In orderto findoutthe degree of signal degradationinthe powerline channel betweentwoaccess
point,software simulationisdone usingMATLABprogrammingtoobtainthe transferfunctionwhose
magnitude (dB) Vsfrequencyplotgivesthe attenuationin the signal strengthandangle (radian) Vs
frequencyplotgivesthe phase distortionordelay.The MATLABprogram isas givenbelow.
%AttenuationandPhase measurement
h=rfckt.twowire('EpsilonR',0.8,'Linelength',5,'Radius',0.63e-003,'separation',4.06e-
003,'sigmaCond',5.8e07,'sigmaDiel',1e-05,'stubmode','Shunt','termination','open');
freq=[1e6:1e6:3e7];analyze(h,freq);
figure
plot(h,'s21','dB');legend show
figure
plot(h,'s21','Angle (radians)');legend show
impulse response
%Impulse response of powerline channel
G =[0.9981-0i 0.9978-0.0095i 0.9974-0.0191i 0.9969-0.0291i 0.9960-0.0397i 0.9948-0.0509i
0.9932-0.0633i 0.9909-0.0771i 0.9877-0.0929i 0.9831-0.1115i 0.9764-0.1341i 0.9660-0.1623i
0.9489-0.1992i 0.9182-0.2492i 0.8562-0.3189i 0.7118-0.4053i 0.3687-0.3819i
0.1671+0.1607i 0.5660+0.4316i 0.7984+0.3643i 0.8927+0.2830i 0.9361+0.2234i
0.9589+0.1803i 0.9722+0.1480i 0.9805+0.1227i 0.9860+0.1022i 0.9898+0.0851i
0.9925+0.0703i 0.9944+0.0572i 0.9958+0.0454i 0.9968+0.0345i];
w =[0:1e6:3e7];
Ts=pi/3e7;
datafr =idfrd(G,w,Ts);
datf =iddata (datafr);
dat =ifft (datf);
plot(dat)
G =[0.9981-0i 0.9978-0.0095i 0.9974-0.0191i 0.9969-0.0291i 0.9960-0.0397i
0.9948-0.0509i 0.9932-0.0633i 0.9909-0.0771i 0.9877-0.0929i 0.9831-0.1115i
0.9764-0.1341i 0.9660-0.1623i 0.9489-0.1992i 0.9182-0.2492i 0.8562-0.3189i
0.7118-0.4053i 0.3687-0.3819i 0.1671+0.1607i 0.5660+0.4316i 0.7984+0.3643i
0.8927+0.2830i 0.9361+0.2234i 0.9589+0.1803i 0.9722+0.1480i 0.9805+0.1227i
0.9860+0.1022i 0.9898+0.0851i 0.9925+0.0703i 0.9944+0.0572i 0.9958+0.0454i
0.9968+0.0345i];
w =0:1e6:3e7;
Ts=pi/3e7;
datafr =idfrd(G,w,Ts);
datf =iddata(datafr);
dat =ifft(datf);
plot(dat)](https://image.slidesharecdn.com/matlab-150518140520-lva1-app6892/85/Mat-lab-for-bplc-1-320.jpg)
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Understanding RF Fundamentals and the Radio Design of Wireless Networks
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EEL316: ASK
1. The document describes an experiment demonstrating principles of direct sequence spread spectrum (DSSS) transmission.
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This document discusses sampling and analog-to-digital conversion. It begins by introducing sampling and the sampling theorem, which states that a continuous-time signal can be uniquely represented by its samples if the sampling frequency is greater than twice the highest frequency present in the signal. It then describes the components and process of analog-to-digital conversion, including periodic sampling, quantization, encoding, and the effects of aliasing if the sampling rate is too low. It also discusses digital-to-analog conversion and reconstruction of the original analog signal from its samples.
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Adaptive Array Antennas (AAAs) are expected to play a key role in meeting the demands of the wireless communication systems of the future. AAAs in cognitive relay network is proposed to reduce the symbol error rate and improve the system performance. Many algorithms such as wiener solution and least mean square (LMS) will be explained to show how AAAs in cognitive relay network achieves this object. AAAs at different locations will be investigated under AWGN and Rayleigh fading channel. Moreover, enhancement the system performance by showing the effect of increasing the number of AAAs element at the relay node, increasing the source gain and decreasing the relay gain. In addition, increasing the rate adaptation and number of iterations in LMS algorithm has significant improvement in the system.
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Adaptive Array Antennas (AAAs) are expected to play a key role in meeting the demands of the wireless communication systems of the future. AAAs in cognitive relay network is proposed to reduce the symbol error rate and improve the system performance. Many algorithms such as wiener solution and least mean square (LMS) will be explained to show how AAAs in cognitive relay network achieves this object. AAAs
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This document summarizes a simulation of a digital communication system using Monte Carlo simulation methods. Key aspects include:
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PERFORMANCE EVALUATION OF ADAPTIVE ARRAY ANTENNAS IN COGNITIVE RELAY NETWORK
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Delta-sigma modulation is a technique used in analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to convert signals. It works by transmitting only the changes between samples rather than the actual samples. For ADCs, oversampling reduces noise and noise shaping pushes it outside the bandwidth of interest. Simulink was used to model a second-order sigma-delta ADC, showing the input signal, output, and how power spectral density varies with frequency and oversampling ratio. The results demonstrate that higher oversampling ratios improve signal-to-noise ratio and power spectral density as predicted by theory.
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For ease of analog or digital information transmission and reception, modulation is the foremost important technique. In the present project, we’ll discuss about different modulation scheme in digital mode done by operating a switch/ key by the digital data. As we know, by modifying basic three parameters of the carrier signal, three basic modulation schemes can be obtained; generation and detection of these three modulations are discussed and compared with respect to probability of error or bit error rate (BER).
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Mat lab for bplc
- 1. In orderto findoutthe degree of signal degradationinthe powerline channel betweentwoaccess point,software simulationisdone usingMATLABprogrammingtoobtainthe transferfunctionwhose magnitude (dB) Vsfrequencyplotgivesthe attenuationin the signal strengthandangle (radian) Vs frequencyplotgivesthe phase distortionordelay.The MATLABprogram isas givenbelow. %AttenuationandPhase measurement h=rfckt.twowire('EpsilonR',0.8,'Linelength',5,'Radius',0.63e-003,'separation',4.06e- 003,'sigmaCond',5.8e07,'sigmaDiel',1e-05,'stubmode','Shunt','termination','open'); freq=[1e6:1e6:3e7];analyze(h,freq); figure plot(h,'s21','dB');legend show figure plot(h,'s21','Angle (radians)');legend show impulse response %Impulse response of powerline channel G =[0.9981-0i 0.9978-0.0095i 0.9974-0.0191i 0.9969-0.0291i 0.9960-0.0397i 0.9948-0.0509i 0.9932-0.0633i 0.9909-0.0771i 0.9877-0.0929i 0.9831-0.1115i 0.9764-0.1341i 0.9660-0.1623i 0.9489-0.1992i 0.9182-0.2492i 0.8562-0.3189i 0.7118-0.4053i 0.3687-0.3819i 0.1671+0.1607i 0.5660+0.4316i 0.7984+0.3643i 0.8927+0.2830i 0.9361+0.2234i 0.9589+0.1803i 0.9722+0.1480i 0.9805+0.1227i 0.9860+0.1022i 0.9898+0.0851i 0.9925+0.0703i 0.9944+0.0572i 0.9958+0.0454i 0.9968+0.0345i]; w =[0:1e6:3e7]; Ts=pi/3e7; datafr =idfrd(G,w,Ts); datf =iddata (datafr); dat =ifft (datf); plot(dat) G =[0.9981-0i 0.9978-0.0095i 0.9974-0.0191i 0.9969-0.0291i 0.9960-0.0397i 0.9948-0.0509i 0.9932-0.0633i 0.9909-0.0771i 0.9877-0.0929i 0.9831-0.1115i 0.9764-0.1341i 0.9660-0.1623i 0.9489-0.1992i 0.9182-0.2492i 0.8562-0.3189i 0.7118-0.4053i 0.3687-0.3819i 0.1671+0.1607i 0.5660+0.4316i 0.7984+0.3643i 0.8927+0.2830i 0.9361+0.2234i 0.9589+0.1803i 0.9722+0.1480i 0.9805+0.1227i 0.9860+0.1022i 0.9898+0.0851i 0.9925+0.0703i 0.9944+0.0572i 0.9958+0.0454i 0.9968+0.0345i]; w =0:1e6:3e7; Ts=pi/3e7; datafr =idfrd(G,w,Ts); datf =iddata(datafr); dat =ifft(datf); plot(dat)