ECE Colloquium - Irons Lecture - Prof. Scott Acton, University of Virginia
This is a Henry R. and Gladys V. Irons Endowed Lecture
The next ECE Colloquium is Wednesday, November 15th from 10:20 - 11:40 in COR - 101 and Dr. Scott Acton from the University of Virginia will be presenting. He will be presenting a talk titled, Visual Revolution: The Power of Images, Video, and Machine Learning. More information can be found in the flyer below.
Anand Sarwate will receive Outstanding Engineering Faculty Award
Dean of Engineering Alberto Cuitino announced that ECE Professor Anand Sarwate will receive the Outstanding Engineering Faculty Award. The award is bestowed in recoginition of Prof. Sarwate's exceptional performance in teaching and outstanding research. The award recognizes the strong commitment and dedication of exceptional faculty. The award will be presented at 4 PM on Monday November 20th at the SOE Faculty Awards ceremony.
Congratulations on this excellent achievement!
ECE faculty Shriram Ramanathan received a new grant from DARPA
Rutgers IEEE Micromouse teams took 1st and 3rd place in MIT Micromouse Competition
On October 7th, 2023, two teams, "RATZ" and "RemIEEE," representing the Rutgers IEEE Micromouse division, participated in a Micromouse Competition held at the Massachusetts Institute of Technology (MIT). The primary objective of this competition was to construct a robotic "mouse" capable of autonomously navigating to the central 2x2 portion of a 16x16 maze in the shortest time possible. The event was organized by IEEE Region 1 and was hosted as a part of the IEEE MIT Undergraduate Research Technology Conference.
Before the official competition commenced, teams were granted the opportunity to conduct test runs with their robot mice and make any necessary adjustments. Subsequently, all participating teams gathered in a lecture hall to provide brief presentations about the design and functionality of their robotic creations.
During the official competition, each team was allotted 10 minutes to make attempts with their robot mice. The starting point for each mouse was the bottom-left corner of the maze, and if teams needed to reset their runs, they were required to place their mice back in this initial position. Judges were responsible for monitoring the remaining time for each team and assessing how close each team's mouse came to reaching the center of the maze. As none of the mouse robots successfully reached the maze's center, the rankings were determined by the proximity of each mouse to the center rather than the time taken. Team RATZ secured first place by coming closest to the center of the maze, while team RemIEEE claimed third place for having the third closest distance to the center.
ECE BS/MS Information Session
ECE Students,
Learn how ECE Undergraduate Students can accelerate their careers by completing a Master’s degree in as little as one year!
- Exposure to more advanced topics
- Entry-level jobs for MS students usually offer higher salaries and involve more interesting work
- Seven ECE MS specializations including new Machine Learning
Women in ECE Event
ECE Faculty Dario Pompili received a new supplement award from NSF
This Supplement is awarded by the National Radio Dynamic Zones (NRDZ) program and will focus on NextG Spectrum Sharing for O-RAN-based Emergency Networking via Deep Multi-Agent Reinforcement Learning.
Overview:
The Next radio Generation (NextG) of mobile networks, 6G and beyond, is expected to speed up the transition from monolithic and inflexible networks to agile and distributed networking elements that rely on “virtualization”, “softwarization”, openness, intelligent and yet fully interoperable Radio Access Network (RAN) components. Within the current virtual RAN (vRAN) and cloud RAN (C-RAN) concepts, spectrum sharing offers a natural solution to provide more efficient utilization of the spectrum by enabling different users or systems to share the same frequency bands dynamically. However, the traditional RAN framework involves large vendors creating proprietary hardware and software. As a result, in 2018, the O-RAN alliance was formed to realize the NextG cellular networks with flexible multi-vendor network infrastructure to the telecom operators. By deploying O-RAN, the network operators can significantly reduce the operational cost in a dense environment compared to vRAN and C-RAN. Due to the dynamic nature of spectrum policies and the unpredictability of unlicensed usage, spectrum sharing will require RANs to change their operational parameters intelligently and according to the current spectrum context. Although existing RANs do not allow for real-time reconfiguration, the fast-paced rise of the open RAN movement and of the O-RAN framework for NextG networks, where the hardware and software portions of the RAN are logically disaggregated, will allow seamless reconfiguration and optimization of the radio components.
Intellectual Merit:
In light of the above, a Multi-Agent Reinforcement Learning (MARL)-based cross-layer communication framework, “RescueNet”, was developed by the PI for self-adaptation of nodes in emergency networks. The use of mission policies, which vary over time and space, enabled graceful degradation in the Quality of Service (QoS) of the incumbent networks (only when necessary) based on mission policy specifications. Specifically, RescueNet was designed to solve the spectrum-sharing problem in emergency networking; however, this framework has limitations. Firstly, it converges slowly and requires a large amount of data to train, indicating that the RL agents need to interact with the environment extensively, which is time and bandwidth consuming. Secondly, RescueNet adopts Q-learning as the basic policy. Nonetheless, Q-learning cannot handle continuous action spaces, which poses additional constraints to the applications that RescueNet can handle.
To deal with these limitations, as part of this Supplement Award, the PI will perform two research tasks. In Task 1, the PI will focus on adopting MARL for emergency networking in O-RAN architecture. Specifically, in Task 1.A, the PI proposes knowledge sharing among agents to aid RescueNet in terms of convergence rate. Experienced agents will transfer their knowledge to new agents so that the new agents can avoid starting from scratch. In Task 1.B, the PI proposes to combine RescueNet with O-RAN leveraging its Near-Real-Time (NRT) Radio Intelligent Controller (RIC), and proposes intelligent spectrum management unit and policy decision unit for NRT decision making. Then, in Task 2, the PI will propose deep Hierarchical Multi-Agent Actor-Critic (HMAAC) by adding a high-level policy for coordination to the Multi-Agent Actor Critic (MAAC) framework to tackle the convergence rate and action space problems. With the help of deep neural networks and hierarchical structures, coordination and knowledge sharing will become more efficient. In Task 2.A, the PI will propose decentralized HMAAC, where the high-level policy is assigned to multiple agents to avoid single-point-failure. Finally, in Task 2.B, the PI will propose to extend HMAAC to the multi-team scenario where multiple teams can cooperate together. In this setting, another hierarchy will be added to HMAAC to coordinate among teams; furthermore, a communication reward will be included to encourage the agents to maintain communication.
More info can be found here: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030101&HistoricalAwards=false
REFAI - "Ultra Low-Power Machine Learning via Hardware and Software Co-design"
Guest Lecture - Prof. Wujie Wen, North Caroline State University
You're invited to this Rutgers Efficient AI (REFAI) Seminar