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NOC15 July-Sep EC05
เข้าร่วมเมื่อ 14 เม.ย. 2015
Lecture 43: Capacity of MIMO Wireless Systems
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IIT Kanpur Certificate Program on Python & QISKIT for Data Science, Data Analytics, Machine Learning and Quantum Computing
1st March to 28th March 2025
Welcome to the IITK Certificate Program on Python QISKIT for Data Science (DS), Data Analytics (DA), Machine Learning (ML) and Quantum Computing (QC). Data Science provides some of the most promising career opportunities for students/ professionals and Data Analytics skills are highly sought-after by industry. Together with Machine learning (ML) to learn from data and Quantum computing that harnesses the principles of Quantum Mechanics, these fields are set to revolutionize Business, Information Processing and Machine Intelligence. This cutting-edge school will introduce participants to the rigorous theory, algorithms and scientific methods to derive actionable insights from large datasets via cutting-edge algorithms for Data Analytics, Machine Learning and Quantum Computing. The school also includes extensive assisted PYTHON programming projects where participants will gain hands-on experience in data analysis, exploration and visualization using practical datasets and the latest PYTHON packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SEABORN, SCIKIT-LEARN and QISKIT. The school also includes problem solving sessions to prepare for tests/ job interviews in DS, DA, ML and QC.
All modules will be held on evenings and weekends for the convenience of participants. The extensive projects and PYTHON/ QISKIT programming for DATA SCIENCE (DS), DATA ANALYTICS (DA), Machine Learning (ML) and Quantum Computing (QC) are of significant value to participants of all backgrounds. Do not miss the added value in Flash and Early bird registration!
Benefits of this program
UG/ PG students: Learn the latest programming techniques in PYTHON and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKIT-LEARN, SEABORN, QISKIT, together with practical DS/ DA/ ML/ QC skills!
PhD Scholars/ Faculty members: Use PYTHON, and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKIT-LEARN, SEABORN, QISKIT, for research and also to establish virtual labs or for project guidance in DS/ DA/ ML/ QC Technologies with real world datasets!
Industry Professionals: Take your skills to the next level by learning PYTHON, and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKITLEARN, SEABORN, QISKIT, together with principles of Module Design and Analysis using Practical Data Sets for DS/ DA/ ML/ QC Technologies!
More information can be found at website: www.iitk.ac.in/mwn/DSDAQ/index.html
Contact us:
e-mail: mimo5G.iitk@gmail.com
Whatsapp: +91 7054568434
Regards,
Team IIT Kanpur Certification Programs
On behalf of
Prof. Aditya K. Jagannatham
Professor
IIT Kanpur
e-mail: mimo5G.iitk@gmail.com
www.iitk.ac.in/mwn/DSDAQ/index.html
IIT Kanpur Certificate Program on Python & QISKIT for Data Science, Data Analytics, Machine Learning and Quantum Computing
1st March to 28th March 2025
Welcome to the IITK Certificate Program on Python QISKIT for Data Science (DS), Data Analytics (DA), Machine Learning (ML) and Quantum Computing (QC). Data Science provides some of the most promising career opportunities for students/ professionals and Data Analytics skills are highly sought-after by industry. Together with Machine learning (ML) to learn from data and Quantum computing that harnesses the principles of Quantum Mechanics, these fields are set to revolutionize Business, Information Processing and Machine Intelligence. This cutting-edge school will introduce participants to the rigorous theory, algorithms and scientific methods to derive actionable insights from large datasets via cutting-edge algorithms for Data Analytics, Machine Learning and Quantum Computing. The school also includes extensive assisted PYTHON programming projects where participants will gain hands-on experience in data analysis, exploration and visualization using practical datasets and the latest PYTHON packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SEABORN, SCIKIT-LEARN and QISKIT. The school also includes problem solving sessions to prepare for tests/ job interviews in DS, DA, ML and QC.
All modules will be held on evenings and weekends for the convenience of participants. The extensive projects and PYTHON/ QISKIT programming for DATA SCIENCE (DS), DATA ANALYTICS (DA), Machine Learning (ML) and Quantum Computing (QC) are of significant value to participants of all backgrounds. Do not miss the added value in Flash and Early bird registration!
Benefits of this program
UG/ PG students: Learn the latest programming techniques in PYTHON and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKIT-LEARN, SEABORN, QISKIT, together with practical DS/ DA/ ML/ QC skills!
PhD Scholars/ Faculty members: Use PYTHON, and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKIT-LEARN, SEABORN, QISKIT, for research and also to establish virtual labs or for project guidance in DS/ DA/ ML/ QC Technologies with real world datasets!
Industry Professionals: Take your skills to the next level by learning PYTHON, and various packages such as NUMPY, LINALG, MATPLOTLIB, PANDAS, SCIKITLEARN, SEABORN, QISKIT, together with principles of Module Design and Analysis using Practical Data Sets for DS/ DA/ ML/ QC Technologies!
More information can be found at website: www.iitk.ac.in/mwn/DSDAQ/index.html
Contact us:
e-mail: mimo5G.iitk@gmail.com
Whatsapp: +91 7054568434
Regards,
Team IIT Kanpur Certification Programs
On behalf of
Prof. Aditya K. Jagannatham
Professor
IIT Kanpur
e-mail: mimo5G.iitk@gmail.com
มุมมอง: 28 977
วีดีโอ
Lecture 46: Transmission in Multicarrier Systems
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Lecture 49: Schematic Represntation of OFDM Transmitter and Receiver
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Lecture 47: FFT/IFFT Processing in OFDM
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Lecture 48: Cyclic Prefix in OFDM Systems
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Lecture 44: SVD based MIMO Transmission
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Lecture 45: Orthogonal Frequency Division Multiplexing (OFDM)
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Lecture 50: BER Performance of OFDM Systems
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Lecture 42: SVD in MIMO
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Lecture 37: BER Performance of ZF Receiver
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Lecture 40: BER of Alamouti Coded System
มุมมอง 17K9 ปีที่แล้ว
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Lecture 38: Transmit Beamforming in MISO Systems
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Lecture 39: Alamouti Code and Space-Time Block Codes
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Lecture 41: Singular Value Decomposition (SVD)
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Lecture 36: MIMO Receivers
มุมมอง 35K9 ปีที่แล้ว
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Lecture 34: Multiple Input Multiple Output (MIMO) Systems
มุมมอง 121K9 ปีที่แล้ว
Lecture 34: Multiple Input Multiple Output (MIMO) Systems
Lecture 32: Multipath Diversity in CDMA Systems
มุมมอง 13K9 ปีที่แล้ว
Lecture 32: Multipath Diversity in CDMA Systems
Lecture 31: Analysis of Multi-user CDMA
มุมมอง 18K9 ปีที่แล้ว
Lecture 31: Analysis of Multi-user CDMA
Lecture 29: CDMA Codes: Properties of PN Sequences
มุมมอง 31K9 ปีที่แล้ว
Lecture 29: CDMA Codes: Properties of PN Sequences
Lecture 27: Chip Time and Bandwidth Expansion in CDMA
มุมมอง 21K9 ปีที่แล้ว
Lecture 27: Chip Time and Bandwidth Expansion in CDMA
Lecture 26: Introduction to Code Division Multiple Access (CDMA)
มุมมอง 87K9 ปีที่แล้ว
Lecture 26: Introduction to Code Division Multiple Access (CDMA)
Lecture 22: Delay Spread and Inter Symbol Interference
มุมมอง 30K9 ปีที่แล้ว
Lecture 22: Delay Spread and Inter Symbol Interference
Lecture 23: Coherence Bandwidth of Wireless Channel
มุมมอง 32K9 ปีที่แล้ว
Lecture 23: Coherence Bandwidth of Wireless Channel
Lecture 24: Mobility and Doppler Effect in Wireless Channels
มุมมอง 29K9 ปีที่แล้ว
Lecture 24: Mobility and Doppler Effect in Wireless Channels
Lecture 25: Impact of Doppler Effect on Wireless Channel
มุมมอง 19K9 ปีที่แล้ว
Lecture 25: Impact of Doppler Effect on Wireless Channel
Calculation is wrong.... Please cross check @23:02
what is the mean of P in this video? I try to make my homework but the threshold voltage is 0.13. And if voltage greater than 0.13 the transmitted bit is 0. AWGN channel has 5dB SNR. I must derivate a probability function for the situation that is the transmitted bit is 0 but received bit is 1. Can anybody help me
my professor copied everything from your lectures . and he has the audacity to warns us from copying assignments
clear and NICE!! thank you very much 😍😍😍😍😍
thanks you very much sir for such a beautiful explanantion.
12:50 Does anyone have any documents on how to get that solution? I am a bit confusion there
Hey @Lum, were you able to find the steps to this solution?
My question here is that all these derivation are still based on BPSK modulation or for any MCS in general?
hi, u got any answer for this?
excellent
At 17:40 1/2*e^(-5) = 3.4 * 10^(-3) ..... what am I missing?
YOu are a king
why does u equals sqrt(2/mu)tan(theta)??
8:22
YOU'RE A GREAT LECTURE, THIS FIELD IS NOT EASY BUT YOU'RE MAKING IT AWESOME FOR US
Dears, Is ( L: number of antennas ) represent the number of antennas of receiver or transmitter or all antennas in the systems?
reciever
at 5:40 why does the lower limit of integration becomes( p/standard deviation) instead of p... can any one make it clear please
because we have to use new limits of integration when we substitute t=n/sigma. The previous limits belong to variable n so by substituting n limits of integration in the new variable t =n/sigma we get new limits as infinity and p/standard deviation.
गुरू गोविन्द दोऊ खड़े, काके लागूं पांय। बलिहारी गुरू अपने गोविन्द दियो बताय।। u r great!! sir
Shouldn’t the conclusion be restricted to BPSK only?
good question. I have the same consideration.
hi, u got any explanation for this?
You are God !!!
Sir, I love you.
can anyone give the matlab code for implementing the models for calculation of ber of various digital modulation techniques such as ASK, FSK, PSK, M-PSK. Also implement models to predict propagation losses for communication microwave signals
thank you sir
5:12 SHOULD E0-E(L-1) OR E1-EL ??
Doesn't matter actually, what matters is that total links must be L, so both are same but you have to follow same convention throughout.
At the end 23:00 (1-lamda)^2 should be divided by 4.
sir,when you are talking about average power it is whether rms power because average power of sinusoidal wave is zero
At 17:32 Q(x) <= 0.5 exp( -0.5 x*x ) ----------> EQ #0 for 10 dB SNR, SNR = 10 Pe <= 3.91*10^(-4) ----------> EQ #1 At 14:42: Pe = 7.82 * 10^(-4) ------------> EQ #2 The result from EQ #1 is less than EQ #2 But according to EQ #0: Pe <= 0.5 exp( -0.5 x*x ) Can someone explain what I am missing here? Thanks!
It's a mistake. the correct answer should be 3.37*10^(-3). 3.91*10^(-4) = 1/2 * 7.82*10^(-4). Looks like the numbers got mixed up in his notes.
So this video is nothing but the derivation of the formula that had been written directly in the previous video right?
thanks
thanks
thank you
Sir,Your work is marvellous and magnificent
where are u working currently ??
This proof is unnecessarily complicated. A much simpler proof can be got by swapping integrals, integrating rayleigh first and then completing the squares. You do have to use the expression for variance of gaussian also.
Could you provide me with the proof you mentioned?? It would be helpful for my research...
@@isisisis123 I apologize to the professor for my choice of words. I was probably in a bad mood when I posted this. But it is true that a simpler proof is there. Let me try to explain it in subsequent posts.
@@isisisis123 Let us look at 3:40. Swap the integrals. Inner integral will be from 0 to sqrt(t/mu). Integrating this for 2ae^(-a^2) is easy. You get 1 - e^(-(t^2)/mu). The rest should be a simple Gaussian integral manipulation .Write it out and see.
@@konchady1 can you send me in telegram?
sir what about BER in MIMO fading channels
Very thorough. Thank you
Indian . you are the worst one who try to explain communication theory. You never concern about the quality of your lectures but view number of your videos. Actually, you are disturbing people from finding the highest quality information
Thank you very much professor Aditya!
Thank-you so much!
sir, i need BER,SNR and achievable rate for wireless channel from equation y=hx +n how to find?
sir , really this video for my project works
Deep intuition for Deep Fade !!
What would the BER be if the receiver didn't have the CSI and could not perform MRC? So instead of SNR after MRC you had some sub-optimal scheme at the receiver? Great lectures by the way, I am from a maths background going into comms and these lectures have made things SO much clearer than any of the literature I have found!
You change the complex math to very intuition view, you are the best teacher.
Sir please explain Exact BER Expression for Nakagami Fading Wireless Channel
Thank you for the course. One thing I want to correct, w=h/||h||^2 because it should be that w^H*h=1 so w=h/(|h1|^2 + |h2|^2) no sqrt needed.
I think we need to think here more on the lines of what the weighting vector must achieve to get maximum SNR. Remember what Mr. Adithya mentioned here, in order to maximize SNR, the cos component must be maximised, this can be done by making weights' vector be in same direction as h vector. How can this be done in simple way? Well you can assume w to be normalised unit vector in direct of h vector. What h/||h|| achieves is normalisation of h vector thus convinietly getting w vector that satisfies max SNR condition. For more info, please refer to GRAHM SCHMIDTT ORTHOGONALIZATION operation.
Highly recommended.
@anisovitch anis Think of transpose operation in real domain, it is nothing but rotating our vectors. Now, extend this to complex domain, rotation in complex domain is nothing but taking conjugate of that number (that is we rotate the number by 90 deg clock or anti clockwise).
Sir, Very helpful.
Best explaination... very thorough thanks!
Are you sure that the 1/2 coefficient in the approximation belongs there? The approximated value in the example seems to be pretty much exactly 1/2 of the exact value.
You are right, the 1/2 shouldn't be there
Check the Wikipedia article. The half is there for q function approx.
thanks for the lecture
in 1+lemda/2, if we approx the 1/snr=0, then can we approx the 1/snr=0 in (1-lemda/2), if not then why?
Theoretically, this is from the limit theory or approximations. Practically, when you're approximating something, you still want to keep the relative error least (|(original value - approx value)|/original value) to be least. When you make the 1/SNR term 0 in the 1+Lambda/2, the loss in accuracy is negligible. When you make the 1/SNR term 0 in the 1-Lambda/2 approximation, we loose the entire value while approximating, which shouldn't be done. Some value was tending to 0, but wasn't 0. And we approximated as zero. Now, it'll create havoc everywhere else, cuz we do like to divide things a lot in engineering. Hope it helps
I know you would not reply to any one but i wanna say something for you.I RESPECT YOU FROM BOTTOM OF MY HEART.THANKSSSSSSSSSSSSSSSSSSSSS