Bridging the Skill and Standards Gap in ML for Communication Networks
Vishnu Ram offers insights on making contributions in ML for communication networks, highlighting opportunities and projects at ITU. Student feedback emphasizes learning new concepts and recommending the projects to others.
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Bridging the skill and standards gap Vishnu Ram vishnu.n@ieee.org https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/SitePages/Home.aspx 1
AIM To utilize the real opportunity for making strong contributions in the use of ML in communication networks. To gain early movers advantage 2
S-killing! ITU offers Student projects ITU is offering guidance to uni students for doing relevant projects in AI/ML in 5G. List of projects is described in ML5G-I-174 How to join: 15-20 students actively contributing at any point of time Across 4-5 countries 7 student contributions published from 4 different countries. Publicly accessible from ITU FG sharepoint site. Send me an email
Example-2 ML5G-I-170-R1.docx The contribution was made on Use cases and solutions for migrating to IMT-2020 networks in emerging markets Talk to the mentor 4
Example-2 (feedback with consent) Was the project relevant to you? If yes, how was it relevant? Yes, because we have applied theoretical concepts delivered to us in class practically. As it stands, we plan to use the project as our final year project in school. Undertaking the project has been very educative. Did you learn some new concepts? Could you please list any? We learnt a lot of new concepts, including the following: - Future networks including IMT-2020 networks - How to save energy in networks with ML - Using ML to optimize 5G coverage by effectively switching between access nodes - How to distribute ML tasks across UEs, edge devices and IoT devices - Working with edge computers to communicate over servers in the cloud Talk to the mentor 5
Example-3 Talk to the mentor 6
Example-3 (feedback with consent) Were the projects relevant to your students? If yes, how are they relevant? Yes the projects were relevant as it was on machine learning and 5G. I had started learning about 4G communication in our classes and it was really good to learn about 5G technology simultaneously. Did the student learn some new concepts? Could you please list any? I learnt many new concepts which I might never learn in normal university lessons. Some of them are ML Pipeline, Sandbox, MLFO, Orchestration, 5G Architecture, ML use cases for 5G network. Would you recommend it to other students? Have you already done so? I would definitely recommend it to other students. Juniors saw our presentation at the nokia and were inspired. I will encourage them to take up ITU student projects as their 3 year internship project. rd Talk to the mentor 7
Lessons learnt 1. Students are busier than mentors. 2. We need Kool projects. 3. The profs need to be onboard. 4. Some countries need official internship. 5. Some students need credits 6. Some mentors need credits 7. Thinking is outsourced . ..To Google. 8. Gap in opportunities, not in skill 9. The role of open source and SDOs in skill development Talk to the editor 8
References https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-206.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-201.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-197.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-182.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-173-R1.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-170-R1.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/input/ML5G-I-168.docx https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/SitePages/Home.aspx Accessible via guest account for non members of ITU-T 9
Thank you! vishnu.n@ieee.org https://extranet.itu.int/sites/itu-t/focusgroups/ML5G/SitePages/Home.aspx
