CIS 4930/5930 Future Edge Networks and Distributed Intelligence - Spring 2026
Lecture
Course Objectives
- Fundamental Goal: Applying AI/ML to 5G and Beyond. This course is designed to explore how machine learning—particularly deep learning—can be applied in modern wireless networks, especially in the context of 5G and beyond.
- Core Focus: 5G/ORAN and Hybrid Routing Systems. We’ll focus primarily on the 5G network architecture, especially on the Open Radio Access Network (ORAN). One of the central themes will be how to intelligently route models (i.e., model routing) in edge environments—selecting and placing ML models dynamically across the network. This intersects with both model routing and network routing, so students will learn how to design such hybrid systems from the ground up.
- Key Topics and Emphasis on Edge-Side Challenges. The course will also touch on topics such as resource allocation, OFDM, and other essential concepts in edge wireless systems. While we’ll briefly cover elements of the 5G core network (e.g., slicing strategies), the emphasis will be on edge-side challenges.
Prerequisites
While no prior experience with wireless systems is required, students should be comfortable with programming (MatLab and Python will be used frequently) and have a basic understanding of machine learning, including some hands-on experience with deep learning techniques.
Course Material
Computer Accounts
Your Responsibilities
- Check the course website and your email account regularly.
- Understand the lecture slides and reading assignments.
- Uphold academic honesty in completing your assignments, projects, and exams.
- Turn in your projects on time.
- Attend office hours for extra help, as needed.
Course Calendar (Tentative)
| Week |
Date |
Topic |
Notes |
| Introduction |
| 1 |
1/8(Th) |
Course Syllabus [PDF]
L1: Preparation for Learning Edge Networks [PDF](VS Code, GitHub, JSON, and AI)
|
Mandatory first-day attendance.
No-shows will be dropped.
HW 1 Posted on [GHC] |
| TBD |
| 2 |
1/13(Tu) |
L: [PDF][Code]() |
|
| 1/15(Th) |
L: [PDF][Code]()
|
HW 1 DDL at 23:59 PM (ET)HW 2 Posted on [GHC] |
| 3 |
1/20(Tu) |
L: [PDF][Code]() |
|
| 1/22(Th) |
L: [PDF][Code]() |
|
| 4 |
1/27(Tu) |
L: [PDF][Code]() |
|
| 1/29(Th) |
L: [PDF][Code]() |
|
| 5 |
2/3(Tu) |
L: [PDF][Code]() |
|
| 2/5(Th) |
L: [PDF][Code]() |
|
| 6 |
2/10(Tu) |
L: [PDF][Code]() |
|
| 2/12(Th) |
L: [PDF][Code]() |
|
| 7 |
2/17(Tu) |
L: [PDF][Code]() |
|
| 2/19(Th) |
L: [PDF][Code]() |
|
| 8 |
2/24(Tu) |
Proposal presentation. |
|
| 2/26(Th) |
Proposal presentation. |
|
| 9 |
3/3(Tu) |
L: [PDF][Code]() |
|
| 3/5(Th) |
L: [PDF][Code]() |
| 10 |
3/10(Tu) |
L: [PDF][Code]() |
|
| 3/12(Th) |
L: [PDF][Code]() |
|
| 11 |
3/17(Tu) |
No Class Due to Spring Break |
|
| 3/19(Th) |
No Class Due to Spring Break |
|
| 12 |
3/24(Tu) |
L: [PDF][Code]() |
|
| 3/26(Th) |
L: [PDF][Code]() |
|
| 13 |
3/31(Tu) |
L: [PDF][Code]() |
|
| 4/3(Th) |
L: [PDF][Code]() |
|
| 14 |
4/7(Tu) |
Project presentation. |
|
| 4/9(Th) |
Project presentation. |
|
| 15 |
4/14(Tu) |
Project presentation. |
|
| 4/16(Th) |
Project presentation. |
|
| 16 |
4/21(Tu) |
Project presentation. |
|
| 4/23(Th) |
Project presentation. |
|
| 17 |
4/28(Tu) |
No class Due to Final Examination Week. |
|
| 4/30(Th) |
Final Exam (5:30 p.m. – 7:30 p.m)
NO MAKE-UPs
|
|
Grading Policy
-
In-Class Quizzes: 5%
There will be several short in-class quizzes throughout the semester. These quizzes are primarily used to track attendance and encourage active participation.
-
Assignments: 15%
There will be several homework assignments (written and coding-based) spaced out over the course of the semester. All assignments are equally weighted. Submission and other instructions will be posted on Canvas or GitHub.
-
Project Proposal & Presentation: 25%
Students will complete a semester-long group project to solve a challenging real-world edge network problem.
-
Project Proposal (15%) is due by 11:59 PM (ET) on 02/22/2026. The proposal is strictly two pages maximum for the main content; references and appendices may be of unlimited length. Supplementary materials (any type) are allowed up to 50 MB.
-
Proposal Presentation (10%) will take place in the middle of the semester (dates subject to change depending on the number of groups). Each group will have 20 minutes for presentation and 10 minutes for Q&A.
Important policy for proposal presentation:
- In-person presentation only. No make-up or deferred presentations will be approved.
- Multiple time slots will be assigned for each class in random order among groups. Plan ahead when submitting your availability.
-
Final Project Report & Presentation: 45%
-
Final report and code (25%) are due 23:59 (ET) on 04/27/2025. The final report is strictly eight pages maximum for the main content; references and appendices may be of unlimited length. Supplementary materials (any type) are allowed up to 50 MB. Only Python or MATLAB will be allowed for implementations.
-
Final presentation (20%) will take place at the end of the semester (dates subject to change). Each group will have 20 minutes for presentation and 10 minutes for Q&A.
Important policy for final presentation:
- In-person presentation only. No make-up or deferred presentations will be approved.
- Multiple time slots will be assigned for each class in random order among groups. Plan ahead when submitting your availability.
Please see a detailed introduction of Project Proposal & Presentation and Final Project Report & Presentation on the course site.
-
Final Exam: 10%
The final exam will be held on Thursday, Aprial 30, 2025, 5:30 p.m.--7:30 p.m. (ET). We will not have class for the whole week. All questions will be closely related to lecture content. Students may bring one A4-size one-page cheat sheet.
Important policy for the final exam:
- No make-up or deferred exams will be approved. In extreme cases, such requests must be approved in advance by the Dean's Office.
- The cheat sheet is not allowed in a make-up exam.
-
Extra Bonus.
Students are encouraged to prepare submissions to arXiv or major AI/ML/DM conferences based on their projects. Please make an appointment with the instructor prior to any submission plan for a comprehensive evaluation of the research topic. Each submission under the instructor's recognition will gain 7 points on the final grade.
Course Policies
Missed Exam Policy
Unexcused missed exams and HW will be given a grade of 0. See the University Attendance Policy for a discussion of valid reasons to excuse absences (https://registrar.fsu.edu/bulletin/graduate/information/academic_regulations/).
Grade of “I” Policy
Incomplete (“I”) grades should be recorded only in exceptional cases when a student, who has completed a substantial portion of the course and who is otherwise passing, is unable to complete a well-defined portion of a course for reasons beyond the student’s control. Students in these circumstances must petition the instructor and should be prepared to present documentation that substantiates their case.
University Attendance Policy
Excused absences include documented illness, deaths in the family and other documented crises, call to active military duty or jury duty, religious holy days, and official University activities. These absences will be accommodated in a way that does not arbitrarily penalize students who have a valid excuse. Consideration will also be given to students whose dependent children experience serious illness.
Academic Honor Policy
The Florida State University Academic Honor Policy outlines the University's expectations for the integrity of students' academic work, the procedures for resolving alleged violations of those expectations, and the rights and responsibilities of students and faculty members throughout the process. Students are responsible for reading the Academic Honor Policy and for living up to their pledge to "...be honest and truthful and...[to] strive for personal and institutional integrity at Florida State University." (Florida State University Academic Honor Policy, found at http://fda.fsu.edu/Academics/Academic-Honor-Policy).
For this course, in particular, every student must complete his/her assignments, quizzes, and exams independently. Showing your work to your peers or making it accessible to them is considered academic dishonesty. You are responsible for ensuring that your work is adequately protected and not accessible to others.
Americans with Disabilities Act
Students with disabilities needing academic accommodation should:
- Register with and provide documentation to the Student Disability Resource Center (SDRC).
- Bring a letter to the instructor indicating the need for accommodation and the specific type required.
Please note that instructors cannot provide classroom accommodations until appropriate verification from the SDRC has been received. This syllabus and other class materials are available in alternative formats upon request.
For more information about services available to FSU students with disabilities, please contact the SDRC:
Address: 874 Traditions Way, 108 Student Services Building, Florida State University, Tallahassee, FL 32306-4167
Phone: (850) 644-9566 (voice), (850) 644-8504 (TDD)
Email: sdrc@admin.fsu.edu
Website: http://www.disabilitycenter.fsu.edu
Confidential Campus Resources
Various centers and programs are available to assist students with navigating stressors that might impact academic success. These include the following:
- Victim Advocate Program: University Center A, Room 4100, (850) 644-7161, Available 24/7/365, Office Hours: M-F 8-5, https://dsst.fsu.edu/vap
- University Counseling Center: Askew Student Life Center, 2nd Floor, 942 Learning Way. (850) 644-8255, https://counseling.fsu.edu/
- University Health Services: Health and Wellness Center, (850) 644-6230, https://uhs.fsu.edu/
Free Tutoring from FSU
On-campus tutoring and writing assistance is available for many courses at Florida State University. For more information, visit the Academic Center for Excellence (ACE) Tutoring Services’ comprehensive list of on-campus tutoring options at http://ace.fsu.edu/tutoring or contact tutor@fsu.edu. High-quality tutoring is available by appointment and on a walk-in basis. These services are offered by tutors trained to encourage the highest level of individual academic success while upholding personal academic integrity.
Late Policy and Make-up Exams
- Late assignments will not ordinarily be accepted. If, for some compelling reason, you cannot hand in an assignment on time, please contact the instructor as far in advance as possible.
- No credit will be given to late course projects.
- No make-up exams (except under extremely unusual circumstances).
Syllabus Change Policy
Except for changes that substantially affect the implementation of the evaluation (grading) statement, this syllabus is a guide for the course and is subject to change with advance notice.