Syllabus#

Course#

ECE 448 — Software Defined Radios Spring 2027

Instructor#

Dr. Neil Rogers
Office hours: by appointment only
Phone: 937-361-2545

Meeting time & location#

See USAFA class schedule for room assignment

Course overview#

Have you ever thought about the sheer number of wireless signals surrounding you in the modern world? From cell phones to WiFi to Bluetooth — wireless communications enable just about everything you do. This course looks at those signals — how they’re generated, transmitted, and received — through the lens of Software Defined Radios (SDRs).

ECE 447 (Communications Theory) covers the fundamental principles of many methods of communication. This course focuses on the applied side of wireless communications: introducing SDRs, familiarizing you with the benefits and limitations of various SDR platforms and, more importantly, the various software packages used to interact with them. You’ll spend most of your time in GNU Radio, building real flowgraphs against real signals. The course culminates in a substantial, self-defined final project that integrates every concept in the course into a specific wireless application of your choosing.

An applications course#

Watch what the signal does; don’t calculate it. We use real radios and GNU Radio to observe effects, such as what happens when the noise goes up, and, when it helps, tie what we see back to theory. ECE 447 derives communications theory; here we use it. Every lesson starts with a signal to build, receive, or decode, and most of every period is spent in GNU Radio with real hardware and real captures. When a lesson brings in an equation, it is because a block parameter, a sample rate, or a filter needs it.

Course goals#

By the end of this course, you will be able to:

Make use of modern software applications to simulate, receive, and transmit signals using modern modulation and encoding techniques.
Describe the principles of basic signal-processing techniques, such as filtering, interpolation, decimation, and matched filtering.
Implement basic signal-processing techniques in the presence of noise and other non-idealities.
Reverse-engineer, decode, and characterize an unknown RF signal captured off the air.
Define, execute, and defend a self-directed SDR project against your own engineering success criteria.

Learning objectives#

The course is organized around specific, testable learning objectives (LOs). Each LO is assessed at least once. Mastery of every LO earns an “A” on the mastery-based portion of the grade.

Module 1 — Foundations of Detection & Link Budgets#

LO	Description
1.1	I can define noise figure and explain how each stage of a receiver chain, the first above all, sets the system’s noise performance.
1.2	I can compute a link budget for a specified geometry, transmitter, and antenna configuration, and compare it with the signal an SDR shows.
1.3	I can describe signal-space concepts (basis functions, constellations, decision regions) and apply them to a digital modulation scheme.

Module 2 — SDR & GNU Radio Foundations#

LO	Description
2.1	I can describe what a Software Defined Radio is, contrast it with a traditional hardware radio, and identify the SDR used in this course, the PlutoSDR.
2.2	I can identify the roles of the RF frontend, ADC/DAC, and host in an SDR signal chain and describe sample-rate, bandwidth, and dynamic-range trade-offs.
2.3	I can install GNU Radio and use GNU Radio Companion (GRC) to build, inspect, and run a basic flowgraph.
2.4	I can capture a real RF signal using an SDR and interpret its time- and frequency-domain representations.

Module 3 — Analog Modulation#

LO	Description
3.1	I can describe DSB-AM, DSB-SC, and SSB and identify each on a spectrum.
3.2	I can implement an AM modulator and demodulator in GNU Radio and verify demodulated audio against the source.
3.3	I can implement an FM receiver in GNU Radio, demodulate a live broadcast FM signal, and decode its RDS data.
3.4	I can explain and demonstrate the trade-off between bandwidth and noise performance for AM vs. FM.

Module 4 — Digital Modulation#

LO	Description
4.1	I can describe FSK modulation and implement an FSK transmitter and receiver in GNU Radio.
4.2	I can describe PAM theory and identify the pulse-shaping, symbol-rate, and bandwidth trade-offs.
4.3	I can apply matched filtering and symbol-timing recovery to a received PAM signal.
4.4	I can implement a PAM link that transmits and receives ASCII text end-to-end.

Module 5 — Custom Blocks & Midterm#

LO	Description
5.1	I can create a GNU Radio out-of-tree (OOT) module and add a custom Python block to it.
5.2	I can implement a custom signal-processing block from scratch and integrate it into a GNU Radio flowgraph.
5.3	I can find a hidden transmitter in a fox hunt using a PlutoSDR, an antenna, and GNU Radio.
5.4	I can solve capture-the-flag challenges that require finding, capturing, demodulating, and decoding unknown signals with GNU Radio.

Module 6 — Waveform Applications & RF Reverse Engineering#

LO	Description
6.1	I can transmit and receive CW (Morse) using GNU Radio and decode a received CW stream.
6.2	I can decode DTMF tones from a captured audio signal.
6.3	I can describe how HD Radio works and demodulate an HD Radio signal.
6.4	I can implement an mPSK modulator and demodulator (BPSK, QPSK, 8PSK) and recover carrier and symbol timing.
6.5	I can implement FHSS transmission and reception, including hop-sequence synchronization.
6.6	I can perform RF reverse engineering on an unknown signal: identify its modulation, symbol rate, and payload structure.
6.7	I can receive and decode ADS-B messages from aircraft transponders.
6.8	I can receive and decode AIS messages from marine vessels.

Module 7 — Final Project#

LO	Description
7.1	I can describe QAM and OFDM and identify use cases for each.
7.2	I can define the scope, objectives, and measurable success criteria for a self-directed SDR project.
7.3	I can research, design, and implement a wireless communication system using an SDR and GNU Radio.
7.4	I can present my project results in a professional-grade report and live demonstration and defend engineering trade-offs against my own stated success criteria.

Grading#

Grades are based on mastery of the learning objectives, demonstrated by what you build: lab flowgraphs, the midterm, and the final project.

Final grade#

Component	Weight
Final project	40%
Labs	25%
Engagement	20%
Midterm	15%

Mastery-based assessment#

Labs, the midterm, and the final project checkpoints are assessed against the learning objectives on a binary scale:

1: you have mastered the objective.
0: you have not yet mastered the objective.

You may revise your work based on the detailed feedback provided, unless no effort is evident (the work is blank, incomplete, or significantly late).

The bar is simple: I want you to learn this material, and I would rather you learn it late than not at all.

Labs#

Each lab is one packet, one submission, hung off the lesson it follows:

Part 1, Pre-Lab: individual, before the period.
Part 2, Bench: in class, on the hardware.
Part 3, Writeup: after the lab, in report format.

Midterm#

The midterm follows Lesson 11 and has two parts: a fox hunt, finding a hidden transmitter at 915 MHz with your own PlutoSDR, an antenna, and GNU Radio, and a capture-the-flag, a set of recordings of unknown signals to find, demodulate, and decode. It is an individual, take-home effort.

Final project#

The final project is intentionally open-ended. You choose the wireless application, write your own success criteria for A-, B-, and C-level outcomes, and defend the result. Each checkpoint below develops a section of your final report and is graded 1 / 0, with revisions under course policy:

Project proposal: what you want to build, why it is meaningful and feasible, and your success criteria.
Background research: at least four high-quality sources and how your project builds on them.
Design description: your initial design, its critical components, use cases, and test plan.
Prototype description: build and test results, and your plan to finish.
Final report and presentation: an IEEE-quality report (typically 7-10 pages) and a five-minute presentation and live demonstration. Code goes in a GitHub repository.

Engagement credits (EC)#

Engagement is earned in several ways:

Weekly quizzes on Gradescope, 1 EC each. Revisions are allowed.
Practice problems. Graded for a genuine, documented attempt, not for correctness.
Extra Instruction (EI). 1 EC for each session you schedule and attend.
Peer review. 1 EC for reviewing or revising a classmate’s report.
Course improvement. Spotted a bug in a lab packet, an unclear slide, or a broken link on this site? Tell me and it counts.

Request EC for EI and peer review through the running Gradescope assignment.

Grade scale#

A	A−	B+	B	B−	C+	C	C−	D	F
100%	93%	90%	87%	83%	80%	77%	73%	70%	≤ 60%

Course policies#

Instructor philosophy#

You have chosen one of the most difficult majors at USAFA — I applaud your commitment. My role in your journey is to enable your success. My intent is to do this by creating an inclusive environment and putting in the work with you. If you work hard and communicate with me, I will do everything in my power to make sure you succeed.

Have questions? Ask — there is no such thing as a stupid question. Have concerns, especially about your learning environment? Let’s talk. You face many challenges here at USAFA; I am in your corner — so let’s go.

1. Absences#

Communicate with me beforehand in the event of an absence. If you will miss any graded event due to a scheduled absence (SCA, sport team trip, scheduled medical procedure), a makeup plan should be in place before the absence occurs. In the event of bedrest, notify me via email as soon as possible after receiving approval.

2. Academic honor#

Your honor is extremely important. The course’s academic security policies are designed to help you succeed in meeting academic requirements while practicing the honorable behavior our country rightfully demands of its military. Do not compromise your integrity by violating academic security or by taking unfair advantage of your classmates.

3. Assignment availability & late work#

All work will be available through Teams, the course website, and/or Gradescope. A 24-hour grace period is generally observed, after which 25% of available points will be deducted for each calendar day the graded assignment is late. I may waive part or all of this penalty for legitimate, pre-coordinated extenuating circumstances.

4. Authorized resources#

The midterm and final project have their own resource policies described in their assignment packets. When electronic devices are authorized for viewing reference materials during any graded event, use of internet, Teams, and generative AI tools is not allowed unless explicitly permitted.

5. Collaboration#

Collaboration (not copying) on skill checkpoints and assessments is highly encouraged, unless I direct otherwise. A good litmus test: you must be able to explain every step on your submitted work. If you can’t, it’s copying, not collaboration. All help received on graded work must be documented per the documentation policy below.

Collaboration is allowed on the Final Project, but must be explicitly and thoroughly documented per the Dean’s documentation policy.

6. Documentation#

In accordance with the Dean’s policy for documentation, all ECE assignments must have a documentation statement. For team projects, you are not required to document collaboration within your own team. The statement should be clearly identified with the word “Documentation.” If you did not collaborate, write “Documentation: None.” Assignments without a documentation statement are incomplete and may be returned for completion; a 1-day-late penalty (up to 25%) may be assessed in lieu of returning the assignment.

7. Extra Instruction (EI)#

EI is one of the best and easiest ways to succeed in this class. Walk-in EI is not generally available — please book with me directly.

8. Generative AI#

I am pro-AI; however, I expect you to use generative AI platforms (ChatGPT, Copilot, Claude, etc.) as a tool, not as a replacement for your own learning. It will be very clear if you are using AI irresponsibly: these tools frequently generate incorrect solutions to the DSP and RF problems you’ll encounter here, and often produce GNU Radio flowgraphs that don’t quite work or don’t align with our course conventions. If you use generative AI on any assignment, include a documentation statement as outlined in the Documentation policy.

9. Submission standards#

All homework must be submitted electronically.
Skill checkpoints and lab writeups should be submitted in report format with appropriate sections (Introduction, Background, Implementation, Results, Discussion). Tailor sections as needed, but include enough detail to demonstrate mastery.
Use engineering units consistently.
All submissions MUST BE TYPED and well-organized unless specifically noted. Use of LaTeX is encouraged.

Course schedule#

Lessons are topics, not class periods: a lesson takes as many periods as it needs, and its lab follows on the bench. Dates will follow the Spring 2027 academic calendar.