Skip to main content

Wireless Communication Based Projects for M.Tech


Our colleges either assign us projects individually during M.Tech or Ph.D coursework. When working on a project, you should be able to apply theoretical concepts to real-world situations. By applying your theoretical knowledge to a project, you will gain a better understanding of a subject and face new challenges at work. And the primary goal of a researcher, engineer, or scientist is to solve problems or difficulties. On the other hand, project excellence can attract companies or investors.

We'll discuss various project/thesis ideas based on contemporary wireless communication. It will benefit both professors and students.


M.Tech Project/Dissertation ideas:

1. Investigating of different beamforming strategies in millimeter wave band

[1.1 Beamforming, Analog, Digital, and Hybrid Beamforming, ]
[1.2 Millimeter Wave ]

2. Signal processing in Massive MIMO
[2.1 SVD based MIMO transmission, ]
[2.2 Optimal power allocation in MIMO ]

3. Channel modelling for extremely high frequency bands such as UWB or millimeter wave bands
[3.1 Channel model for UWB and mm wave ]

4. Saleh Valenzuala Channel Model
[4.1 Time-delayed saleh valenzuala cluster model for UWB & mm-Wave ]

6. Millimeter wave (mm wave) imaging

7. Modulation Techniques for 5G communication (OFDM & NOMA)

[7.1 OFDM for 4G & 5G ]

8. Device to Device Communication (D2D)

9. Industrial M2M Communication using UWB or millimeter wave band

10. Precoding at downlink OFDM

11. FBMC

12. Detection of AOA & AOD in UWB

13. Possible solutions to overcome limitations of under-water wireless communication

14. UAV (unmanned aerial vehicle)

15. Fog Computing for IoTs

16. Wi-Max (60 GHz)

17. Ad hoc networks

18. Li Fi

19. Traffic management using IoTs

20. Fleet management using IoTs

21. Communication at the network layer (in OSI)

22. LED and modem-based E-Notice board

23. Classification of images using deep learning


(For that project artificial neural network Alex-net may be helpful. Similarly, image-net may also be helpful as well for your work ... The basic difference between machine learning and deep learning is - machine learning only can identify an image from a pre-loaded image database while deep learning can learn from dataset and can make its own decision.)
24. Ionospheric scintillation prediction using sophisticated machine learning algorithm
Click here for more details.


(Python / MATLAB for development of machine learning algorithm , gps signal, etc.)


Also read about
[1] Analog and Digital Communication Mini Projects

<<Previous Page
Next Page>>
#beamforming

People are good at skipping over material they already know!

View Related Topics to







Admin & Author: Salim

profile

  Website: www.salimwireless.com
  Interests: Signal Processing, Telecommunication, 5G Technology, Present & Future Wireless Technologies, Digital Signal Processing, Computer Networks, Millimeter Wave Band Channel, Web Development
  Seeking an opportunity in the Teaching or Electronics & Telecommunication domains.
  Possess M.Tech in Electronic Communication Systems.


Contact Us

Name

Email *

Message *

Popular Posts

MATLAB code for MSK

 Copy the MATLAB Code from here % The code is developed by SalimWireless.com clc; clear; close all; % Define a bit sequence bitSeq = [0, 1, 0, 0, 1, 1, 1, 0, 0, 1]; % Perform MSK modulation [modSignal, timeVec] = modulateMSK(bitSeq, 10, 10, 10000); % Plot the modulated signal subplot(2,1,1); samples = 1:numel(bitSeq); stem(samples, bitSeq); title('Original message signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Plot the modulated signal subplot(2,1,2); samples = 1:10000; plot(samples / 10000, modSignal(1:10000)); title('MSK modulated signal'); xlabel('Time (s)'); ylabel('Amplitude'); % Perform MSK demodulation demodBits = demodMSK(modSignal, 10, 10, 10000); % Function to perform MSK modulation function [signal, timeVec] = modulateMSK(bits, carrierFreq, baudRate, sampleFreq) % Converts a binary bit sequence into an MSK-modulated signal % Inputs: % bits - Binary input sequence % carrierFreq - Carri...

BER vs SNR for M-ary QAM, M-ary PSK, QPSK, BPSK, ...

Modulation Constellation Diagrams BER vs. SNR BER vs SNR for M-QAM, M-PSK, QPSk, BPSK, ... What is Bit Error Rate (BER)? The abbreviation BER stands for bit error rate, which indicates how many corrupted bits are received (after the demodulation process) compared to the total number of bits sent in a communication process. It is defined as,  In mathematics, BER = (number of bits received in error / total number of transmitted bits)  On the other hand, SNR refers to the signal-to-noise power ratio. For ease of calculation, we commonly convert it to dB or decibels.   What is Signal the signal-to-noise ratio (SNR)? SNR = signal power/noise power (SNR is a ratio of signal power to noise power) SNR (in dB) = 10*log(signal power / noise power) [base 10] For instance, the SNR for a given communication system is 3dB. So, SNR (in ratio) = 10^{SNR (in dB) / 10} = 2 Therefore, in this instance, the s...

Constellation Diagrams of ASK, PSK, and FSK

BASK (Binary ASK) Modulation: Transmits one of two signals: 0 or -√Eb, where Eb​ is the energy per bit. These signals represent binary 0 and 1.    BFSK (Binary FSK) Modulation: Transmits one of two signals: +√Eb​ ( On the y-axis, the phase shift of 90 degrees with respect to the x-axis, which is also termed phase offset ) or √Eb (on x-axis), where Eb​ is the energy per bit. These signals represent binary 0 and 1.  BPSK (Binary PSK) Modulation: Transmits one of two signals: +√Eb​ or -√Eb (they differ by 180 degree phase shift), where Eb​ is the energy per bit. These signals represent binary 0 and 1.    Simulator for BASK, BPSK, and BFSK Constellation Diagrams SNR (dB): 15 Add AWGN Noise Modulation Type BPSK BFSK ...

Fundamentals of Channel Estimation

Channel Estimation Techniques Channel Estimation is an auto-regressive process that may be performed with a number of iterations. There are commonly three types of channel estimation approaches. 1. Pilot estimation  2. Blind estimation  3. Semi-blind estimation. For Channel Estimation,  CIR [↗] is used. The amplitudes of the impulses decrease over time and are not correlated. For example, y(n) = h(n) * x(n) + w(n) where y(n) is the received signal, x(n) is the sent signal, and w(n) is the additive white gaussian noise At the next stage, h(n+1) = a*h(n) + w(n) The channel coefficient will be modified as stated above at the subsequent stage. The scaling factor "a" determines the impulse's amplitude, whereas "h(n+1)" represents the channel coefficient at the following stage. Pilot Estimation Method To understand how a communication medium is currently behaving, a channel estimate is necessary. In order to monitor a channel's behavior in practice communication ...

Comparisons among ASK, PSK, and FSK | And the definitions of each

Modulation ASK, FSK & PSK Constellation MATLAB Simulink MATLAB Code Comparisons among ASK, PSK, and FSK    Comparisons among ASK, PSK, and FSK Comparison among ASK,  FSK, and PSK Performance Comparison: 1. Noise Sensitivity:    - ASK is the most sensitive to noise due to its reliance on amplitude variations.    - PSK is less sensitive to noise compared to ASK.    - FSK is relatively more robust against noise, making it suitable for noisy environments. 2. Bandwidth Efficiency:    - PSK is the most bandwidth-efficient, requiring less bandwidth than FSK for the same data rate.    - FSK requires wider bandwidth compared to PSK.    - ASK's bandwidth efficiency lies between FSK and PSK. Bandwidth Calculator for ASK, FSK, and PSK The baud rate represents the number of symbols transmitted per second Select Modulation Type: ASK...

Difference between AWGN and Rayleigh Fading

Wireless Signal Processing Gaussian and Rayleigh Distribution Difference between AWGN and Rayleigh Fading 1. Introduction Rayleigh fading coefficients and AWGN, or additive white gaussian noise [↗] , are two distinct factors that affect a wireless communication channel. In mathematics, we can express it in that way.  Fig: Rayleigh Fading due to multi-paths Let's explore wireless communication under two common noise scenarios: AWGN (Additive White Gaussian Noise) and Rayleigh fading. y = h*x + n ... (i) Symbol '*' represents convolution. The transmitted signal  x  is multiplied by the channel coefficient or channel impulse response (h)  in the equation above, and the symbol  "n"  stands for the white Gaussian noise that is added to the signal through any type of channel (here, it is a wireless channel or wireless medium). Due to multi-paths the channel impulse response (h) changes. And multi-paths cause Rayleigh fa...

Constellation Diagram of FSK in Detail

  Binary bits '0' and '1' can be mapped to 'j' and '1' to '1', respectively, for Baseband Binary Frequency Shift Keying (BFSK) . Signals are in phase here. These bits can be mapped into baseband representation for a number of uses, including power spectral density (PSD) calculations. For passband BFSK transmission, we can modulate signal 'j' with a lower carrier frequency and signal '1' with a higher carrier frequency while transmitting over a wireless channel. Let's assume we are transmitting carrier signal fc1 for the transmission of binary bit '1' and carrier signal fc2 for the transmission of binary bit '0'. Simulator for 2-FSK Constellation Diagram Simulator for 2-FSK Constellation Diagram SNR (dB): 15 Add AWGN Noise Run Simulation ...

Gaussian minimum shift keying (GMSK)

Dive into the fascinating world of GMSK modulation, where continuous phase modulation and spectral efficiency come together for robust communication systems! Core Process of GMSK Modulation Phase Accumulation (Integration of Filtered Signal) After applying Gaussian filtering to the Non-Return-to-Zero (NRZ) signal, we integrate the smoothed NRZ signal over time to produce a continuous phase signal: θ(t) = ∫ 0 t m filtered (Ï„) dÏ„ This integration is crucial for avoiding abrupt phase transitions, ensuring smooth and continuous phase changes. Phase Modulation The next step involves using the phase signal to modulate a high-frequency carrier wave: s(t) = cos(2Ï€f c t + θ(t)) Here, f c is the carrier frequency, and s(t) represents the continuous-phase modulated carrier wave. Quadrature Modulation (Optional) ...