Matlab course for wireless communication engineering

Name: Matlab course for wireless communication engineering
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A step by step the Matlab codes for BER estimations of different Comm. systems like OFDM and NOMA Comm. systems

Created byDr. Khaled Ramadan

Last updated 2/2021

English

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What you'll learn

How to estimate the Bit-Error-Rate (BER) performance of different modulation schemes like BPSK, QPSK, 16QAM, 64QAM, and 256QAM over an Additive White Gaussian Noise (AWGN) channel?
How to generate and add a Rayleigh fading channel based on the Jakes model?
How to mitigate the Rayleigh fading channel effect using different linear equalizer schemes?
How to estimate the Bit-Error-Rate (BER) performance over Single-Input-Single-Output Orthogonal Frequency Division Multiplexing (SISO-OFDM) communication system over a Rayleigh fading channel?
How to extend this work for different Multiple-Input-Multiple-Output (MIMO) configurations like 2×2, and 3×3 MIMO-OFDM communication systems over a Rayleigh fading channel?
A general code for BER estimation in case of Nt×Nr configuration for OFDM communication system over a Rayleigh fading channel will be presented, where Nt, is the number of transmitting antennas, and Nr is the number of receiving antennas.
Finally, the BER estimation for Non-Orthogonal Multiple Access (NOMA), which used in the 5G communication systems will be presented.

Course content

8 sections • 19 lectures • 3h 28m total length

Course Overview2:18
Explore Matlab codes for bit-error-rate estimation of bpsk, qpsk, 16qam, 64qam, and 256qam over awgn for wireless communication engineering. Implement Jake's Rayleigh fading model, study mimo-ofdm and noma for 5g.

1-Introduction2:15
Introduce a MATLAB based single input single output wireless system over an additive white Gaussian noise channel, comparing BPSK, QPSK, 16QAM, 64QAM, 256QAM and BER vs SNR trade-offs.
2-BER estimation for BPSK and QPSK19:42
Learn to implement MATLAB code for BER estimation over an AWGN channel, compare BPSK and QPSK modulations, and visualize BER versus SNR with a semilogy plot.
3-BER estimation for 16, 64, and 256QAM8:15
Explore BER estimation for 16, 64, and 256 QAM in Matlab by modifying modulation and demodulation for higher-order schemes, analyzing BER versus SNR with comm.RectangularQAMModulator, Gray coding, and AWGN.
4-General code for BER estimation13:58
Analyze bit error rate versus signal-to-noise ratio for BPSK, QPSK, 16QAM, 64QAM, and 256QAM over AWGN, using a switch to compare mapping schemes and modulation/demodulation.

1-Introduction1:19
Generate a Rayleigh fading channel with the Jakes model and verify its effect on bit error rate versus SNR, then discuss mitigation with equalizers.
2-BER estimation in case of Rayleigh fading channel without equalization26:15
Simulate bit error rate versus signal-to-noise ratio for BPSK, QPSK, 16QAM, 64QAM, and 256QAM over a Rayleigh fading channel using the Jakes model in Matlab, without equalization.

1-introduction4:33
Explore linear frequency-domain equalizers to mitigate Rayleigh fading in a single-input single-output wireless link, comparing zero-forcing and linear minimum mean square error schemes and their impact on bit error rate.
2-BER estimation in case of Rayleigh fading channel with channel equalization13:13
Mitigate Rayleigh fading in a six-tap Jakes model using frequency-domain linear equalizers. Compare linear zero-forcing and linear MMSE performance on BPSK with FFT-based impulse responses.

1-introduction4:12
Analyze bit error rate of SISO-OFDM system over a Rayleigh fading channel, including random data, mapping, modulation, cyclic prefix, IFFT/FFT, equalization, and demapping across BPSK, QPSK, 16QAM, 64QAM, and 256QAM.
2-BER estimation of SISO-OFDM comm. system over a Rayleigh fading channel21:25
Explore a Matlab siso-ofdm model over a rayleigh fading channel based on jakes, implementing linear zero-forcing and mmse equalizers with ifft/fft and cyclic prefix, and ber versus snr across mappings.

1-introduction4:50
Explore a 2x2 MIMO-OFDM system over a Rayleigh fading channel, derive the frequency-domain transfer functions and Y1, Y2 expressions, and analyze bit error rate versus SNR using linear zero-forcing equalizers.
2-BER estimation of a 2x2 MIMO-OFDM comm. system20:20
Estimate the bit error rate of a 2x2 MIMO-OFDM system under Rayleigh fading using Jakes models, and compare zero-forcing with linear MMSE equalizers for BPSK with cyclic prefix.
3-Introduction5:07
Extend SISO-OFDM to a 3×3 MIMO-OFDM system over a Rayleigh fading channel, detailing Y = H X and BER versus SNR curves with zero-forcing and MMSE equalizers.
4-BER estimation of a 3x3 MIMO-OFDM comm. system19:36
Analyze BER estimation for a 3x3 MIMO-OFDM system under six-tap Rayleigh fading with the Jakes model, exploring multiplexing versus diversity.

1-Introduction3:24
Develop a MATLAB code to estimate the bit error rate for Nt×Nr ofdm systems. Illustrate transmitter and receiver structures over Rayleigh fading.
2-BER estimation of a NtxNr OFDM comm. system23:51
Simulate and code Nt×Nr MIMO-OFDM system over a Rayleigh fading channel using Jakes models, FFT/IFFT, cyclic prefix, and a linear minimum mean square error equalizer for BER vs SNR.

1-Introduction1:46
Explore non-orthogonal multiple access in 5G systems with power-domain multiplexing and successive interference cancellation, and assess bit error rate versus snr under Rayleigh fading with bpsk and lmmse.
2-BER estimation of NOMA communication system11:53
Demonstrates a MATLAB BER estimation for a two-user downlink NOMA system with power-domain multiplexing and successive interference cancellation over a Rayleigh fading channel using SISO-OFDM, CP, and MMSE equalization.

Requirements

Matlab Program

Description

This is course is designed for under and post graduate students associated to the electronics and communication engineering department. It has 10 introductions of 30 minutes, each introduction discusses the theoretical explanation and the simulated figures that obtained through the explanation of the course. This course saves many months of hard work.

Using the Matlab program, you will learn:

1-How to estimate the Bit-Error-Rate (BER) performance of different modulation schemes like BPSK, QPSK, 16QAM, 64QAM, and 256QAM over an Additive White Gaussian Noise (AWGN) channel?

2-How to generate and add a Rayleigh fading channel based on the Jakes model?

3-How to mitigate the Rayleigh fading channel effect using different linear equalizer schemes?

4-How to estimate the Bit-Error-Rate (BER) performance over Single-Input-Single-Output Orthogonal Frequency Division Multiplexing (SISO-OFDM) communication system over a Rayleigh fading channel?

5-How to extend this work for different Multiple-Input-Multiple-Output (MIMO) configurations like 2×2, and 3×3 MIMO-OFDM communication systems over a Rayleigh fading channel?

6-A general code for BER estimation in case of Nt×Nr configuration for OFDM communication system over a Rayleigh fading channel will be presented, where Nt, is the number of transmitting antennas, and Nr is the number of receiving antennas.

7-Finally, the BER estimation for Non-Orthogonal Multiple Access (NOMA), which used in the 5G communication systems will be presented.

Who this course is for:

This is course is designed for under and postgraduate students associated with the electronics and communication engineering department

Matlab course for wireless communication engineering

What you'll learn

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Course content