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Overview
About the project
At the CCSL, we are engaged in research and teaching on wireless communication methods for future wireless communication systems. In future wireless communication 5G and beyond, an extremely high number of heterogeneous devices, such as smartphones, sensors, robots, and vehicles, will communicate with each other. Consequently, the need for higher data rates and lower latency will increase significantly, posing major challenges for the resource allocation. Deep learning and data-driven algorithm approximation schemes have recently received significant attention as means to perform resource allocation with reduced complexity in 5G and beyond networks.
This Machine Learning for Wireless Communication Systems (Internship) project offered at King Abdullah University of Science and Technology (KAUST) ) considers the challenging case of Reflective Intelligent System (RIS) assisted, mmWave, frequency selective, massive MIMO systems with hybrid architecture and develops deep-learning based resource allocation frameworks. In these frameworks, prior data-set observations and deep neural network models will be leveraged to learn the mapping from received measurements to channels, beamformers and power allocations.
Get more details
Visit programme websiteProgramme Structure
- This project aims to improve the overall performance on emerging and beyond 5G wireless systems in boosting system capacity with improved robustness and high data rates, via a low-cost, low-latency, and green implementation. For this purpose, deep learning or machine learning methods shall be applied, which can adapt to the dynamic changes of the wireless communication system and its environment and exploit the past experience to improve the future performance of the system.
- A report detailing system design and simulation results are expected at the end of the program.
Check out the full curriculum
Visit programme websiteKey information
Duration
- Part-time
- 3 months
Start dates & application deadlines
You can apply for and start this programme anytime.
More details
Students can choose their own start and end dates as the program runs across the year
Language
English
TOEFL® IBT 79
Delivered
On Campus
Disciplines
Electrical Engineering View 14 other Short Courses in Electrical Engineering in Saudi ArabiaExplore more key information
Visit programme websiteAcademic requirements
GPA 3.5
English requirements
TOEFL® IBT 79
Student insurance
Make sure to cover your health, travel, and stay while studying abroad. Even global coverages can miss important items, so make sure your student insurance ticks all the following:
- Additional medical costs (i.e. dental)
- Repatriation, if something happens to you or your family
- Liability
- Home contents and baggage
- Accidents
- Legal aid
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Remember, countries and universities may have specific insurance requirements. To learn more about how student insurance work at King Abdullah University of Science and Technology (KAUST) and/or in Saudi Arabia, please visit Student Insurance Portal.
Other requirements
General requirements
- Official Transcript(s) in English from current and previous institutions
- Letter of Recommendation from faculty member in field of study
- Valid Passport(with six months validity)
- CV - Summary of your career, qualifications and education.
- Statement of Purpose-Explain your motivation to become a VSRP intern
Make sure you meet all requirements
Visit programme websiteTuition Fee
To always see correct tuition fees
-
International
FreeTuition FeeBased on the tuition of 0 SAR per year during 3 months. -
National
FreeTuition FeeBased on the tuition of 0 SAR per year during 3 months.
This is a fully-funded internship; it includes tuition, airfare, housing, medical insurance, visa reimbursement and a monthly stipend of $1,000.
Living costs for Thuwal
2270 - 3445 SAR /month
Living costs
The living costs include the total expenses per month, covering accommodation, public transportation, utilities (electricity, internet), books and groceries.
Funding
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