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 ]
5. 5G communication signal processing
[5.1 5G: Current 5G Technology, Frequency Bands, Speed, and Other Factors, ]
[5.2 5G : Theoretical Aspects | Frequency & Spectrum, Speed, Massive MIMO & OFDM ]
6. Millimeter wave (mm wave) imaging
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
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