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DTSTART;TZID=Asia/Kolkata:20220409T103000
DTEND;TZID=Asia/Kolkata:20220409T123000
DTSTAMP:20260601T172641
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SUMMARY:AI/ML algorithms for radio access networks
DESCRIPTION:The Department of Electronics and Communication Engineering is hosting the first instalment of the Distinguished Industry-Academia Interaction Lecture in the ECE DDIAL series. The event is scheduled on April 9\, 2022\, at 10.30 am. Dr. Serene Banerjee\, Master Researcher\, Ericsson Research\, will be the keynote speaker. She will deliver a talk on the topic ‘AI/ML Based Interference Diagnostics and Mitigation for Improved Quality of Service in Radio Access Networks’. \nAbstract of the Talk \nFor coverage and capacity optimization\, Uplink Power Control is one of the key steps\, in addition to antenna tilting and Downlink Power Control. For self-organizing networks\, automated algorithms for Uplink Power Control are a necessity. However\, Uplink Power Control affects the noise in the neighbouring cells. It is important to detect this interference to monitor uplink noise. Uplink noise due to power control manifests as static interference in the channel. The current state-of-the-art baseline model is based on regression models. We have proposed automated detection of static interference in the uplink channel of cells based on machine learning models. We have evaluated the same on customer data on LTE networks with high accuracy. The detected cells are subsequently used to correct the nominal power parameter through a proposed teacher-student model based on the primary cell and its neighbours. This approach shows better performance than the state-of-the-art baseline methods. The dual of static is dynamic interferences and can be attributed to traffic load\, Passive Intermodulation (PIM) and thermal noise\, etc. PIM identification is a major component in troubleshooting modern wireless communication systems. The introduction of carrier aggregation has increased PIM occurrences. Current state-of-the-art approaches include manual rule-based and hardware-based debugging. These approaches can detect the occurrence of PIM\, long after the event occurrence and result in incurring incidental costs. We propose an ensemble of time series-based machine learning and signal processing approaches that can automatically identify PIM in real-time by analyzing Key Performance Indicators (KPI) of the primary cell and its nearest neighbours. We validate our results for various environmental conditions in data available from LTE and 5G consumer networks. We have further extended the work to multi-frequency time series to handle finer time granularities and detect PIM anomalies in an online learning setting. We further propose a self-supervised reinforcement learning approach to predict PIM related anomalies before it happens. We forecast environmental conditions that give rise to PIM based on offline historical data and model to predict future occurrences. Experimental results are on real-world datasets comprising 50\,000+ cells which have shown to accurately predict PIM 60% of the time. To the best of our knowledge\, this is the first work\, where we are able to predict PIM anomalies before they happen. Post PIM-identification\, we propose a binary search-based solution that is amenable to real-time implementation. We show through simulations that this search in tandem with a reinforcement learning-based solution can dynamically mitigate and cancel PIM. Results show that the number of steps to converge\, to identify and mitigate the PIM in uplink frequency is reduced by a large factor. To summarize\, our contributions include using machine learning algorithms for: (1) robust interference classification\, (2) demonstrating p0-nominal recommender as teacher-student model\, (3) a times-series analysis-based PIM identification\, (4) extending the approach to multi-frequency time series\, and for online learning\, (5) demonstrating a self-supervised reinforcement learning approach to predict PIM anomalies before they happen\, and (6) mitigating PIM\, in spite of environmental unknowns\, by employing binary search in conjunction with ML/RL-based approaches. \nAbout the Speaker \nSerene Banerjee\, Master Researcher\, Ericsson Research\, has 17+ years of industrial experience after completing her PhD from the University of Texas at Austin\, under Prof Brian L Evans in 2004. She has completed BTech(H) in Electronics and Electrical Communications Engineering from IIT Kharagpur in 1999. At Ericsson\, she is focusing on developing AI/ML algorithms for Radio Access Networks. Prior to Ericsson\, she has worked with Texas Instruments\, HP\, and Johnson Controls. She has 23 peer-reviewed publications\, 9 granted patents\, and several pending.
URL:https://events.srmap.edu.in/event/ai-ml-algorithms-for-radio-access-networks/
CATEGORIES:Departmental Events,ECE,Events
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BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220414T093000
DTEND;TZID=Asia/Kolkata:20220414T123000
DTSTAMP:20260601T172641
CREATED:20220412T233100Z
LAST-MODIFIED:20220412T233100Z
UID:141773-1649928600-1649939400@events.srmap.edu.in
SUMMARY:A hands-on session on lithium-ion battery: assembly and testing
DESCRIPTION:Allowing students to explore more into their learning domain will invariably enhance their quest for knowledge. The more students are encouraged to experiment with problems\, tools\, and substances they will work with\, the better prepared they are to face any challenge head-on. It is also a better rewarding alternative as against the conventional book learning method. \nTaking the current scenario into account\,imparting hands-on training on cutting-edge developments is an excellent exercise to inculcate scientific spirit in young minds. Such initiatives should be actively promoted to equip students on par with the fast-evolving technological realm. We are delighted to announce that the SRM-Amara Raja Center for Energy Storage Devices\, Department of Electronics and Communication Engineering\, collaborates with NSS Cell-SRM AP to organise a “Hands-on Session on Lithium-ion Battery: Assembly and Testing” with the sponsorship from the DST-SERB Start-up Research Grant (SRG). \nDate: April 14\, 2022 \nTime: 9.30 am to 12.30 pm IST \nWe invite all the interested students to become part of this invigorating initiative and make the best use of this opportunity to receive an enriching training session. The event is also followed by lunch for all the participants.
URL:https://events.srmap.edu.in/event/a-hands-on-session-on-lithium-ion-battery-assembly-and-testing/
CATEGORIES:Departmental Events,ECE,Events
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Kolkata:20220423T102000
DTEND;TZID=Asia/Kolkata:20220423T120000
DTSTAMP:20260601T172641
CREATED:20220420T044600Z
LAST-MODIFIED:20250806T123849Z
UID:141775-1650709200-1650715200@events.srmap.edu.in
SUMMARY:Ultra-low power and agile IC design for novel sensor nodes for the Internet of Things
DESCRIPTION:The Internet of Things (IoT) has greatly increased our awareness of the world and broadened the scope of our engagement with it. In the first episode of the ECE Department Guest Lecture Series\, the Department of Electronics and Communication Engineering at SRM University-AP is organising a lecture on April 23\, 2022\, from 10.20 am to 12.00 noon. Dr Orazio Aiello\, Assistant Professor at the University of Genoa\, Italy\, will engage the audience on the topic Ultra-Low-Power and Agile IC Design for novel Sensor Nodes for the Internet of Things on that day. \nThe vision of a world where pervasive integrated electronic systems embedded in everyday life objects (e.g. household appliances\, surveillance cameras\, healthcare systems) are fully interconnected to collect\, process\, and exchange useful information requires energy-autonomous systems for distributed sensing and data acquisition. The low-cost requirement demands a small area\, low design effort\, digital-like shrinkage across CMOS generations\, and design/technology portability. The possibility to exploit the digital (automated) design flow even for analog building blocks can dramatically reduce the design effort of any system-on-chip that faces the analog signal. Since data processing is digital\, but most signals from the real world are analog\, almost any electronic device that interfaces with the surrounding environment will benefit from the outcomes of this investigation. In this framework\, the talk illustrates the concepts and the design flows which enable the implementation of analog functions by true digital circuits. \nBiography of the speaker: \nOrazio Aiello (Senior Member\, IEEE) received the BSc and MSc degrees (cum laude) from the University of Catania\, Italy\, in 2005 and 2008\, respectively\, the M.Sc. degree (cum laude) from the Scuola Superiore di Catania\, Italy\, in 2009\, and the PhD degree from the Politecnico di Torino\, Italy\, in 2013.\,From 2008 to 2009\, he was an Analog IC Designer and an EMC Consultant for STMicroelectronics\, Castelletto\, Italy. In 2012\, he was a Visiting PhD. A student with Monash University\, Melbourne\, Australia. In 2013\, he was a Research Fellow in a joint project with FIAT-Chrysler Automobiles\, Turin. In 2014\, he joined NXP-Semiconductors\, Nijmegen\, The Netherlands\, as a Mixed Signal IC Designer and an EMC Expert. In 2015 and 2016\, he was a Visiting Fellow with the University of Sydney and the University of New South Wales\, Sydney\, Australia. Since 2015\, he has been working with the Green IC Group\, Department of Electronics and Communication Engineering (ECE)\, National University of Singapore\, where he has also been a Marie Skłodowska-Curie Individual and a Global Fellow leading the ULPIoT project. He is now an Assistant Professor at the University of Genova\, Italy. His main research interests include energy-efficient analogue-mixed signal circuits and sensor interfaces. Dr Aiello is a member of the IEEE CASS Microlearning AdHoc Committee and is/was a Technical Program Committee Member of a number of conferences\, such as NORCAS and APCCAS. \nJoin this informative lecture with Dr Orazio Aiello on April 23\, 2022 at 10.20 am IST.
URL:https://events.srmap.edu.in/event/ultra-low-power-and-agile-ic-design-for-novel-sensor-nodes-for-the-internet-of-things/
CATEGORIES:Departmental Events,ECE,Events,Webinars
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