CESH 280 Unusual RF: Machine Learning for Signal Analysis

AI Acceleration · 3 days · Not assessed

About the course

This is the machine-learning course for RF, aimed at people who already understand signals and want to scale what they can do with them.

Your people train models for modulation recognition and signal classification straight from IQ data, and build deep-learning RF fingerprinting that tells near-identical devices apart by their transmitter signature. They also take on ML-aided demodulation and denoising for the captures that defeat classical methods, automated spectrum triage to sort a crowded band quickly, and the sensible use of large language models as a reverse-engineering copilot.

Throughout, the SPYR synthetic range supplies labelled, answer-keyed IQ for training and honest testing, so models are measured against ground truth rather than wishful thinking. This is an advanced course, and it assumes the RF fundamentals are already in place.

Want to know what to expect in the classroom? Find out more about our training approach and how we turn technical concepts into practical, hands-on skills.

Skills and topics covered

This list shows the various main topics we cover during the course:

ML modulation recognition from IQ
Signal classification from IQ
Deep-learning RF fingerprinting
ML-aided demodulation and denoising
Automated spectrum triage
LLMs as a reverse-engineering copilot
Building labelled IQ datasets
Training and testing against ground truth
Evaluating model performance honestly

What will a new trainee gain?

Your new entrants from a data-science background get a direct route into RF, where they apply familiar machine-learning methods to IQ data and the SPYR range supplies clean labels to learn on. For your organisation, that lets you redeploy data talent onto signals work quickly, rather than waiting years for someone to become an RF specialist the slow way.

What will an experienced team member gain?

An experienced RF engineer learns to automate the tedious, high-volume parts of the work (triage, classification and fingerprinting) and to crack captures that classical demodulation cannot. For your team, that multiplies how many signals one analyst can sensibly handle, and adds a device-identification capability that hand methods simply cannot give you.

Who this course is for

This is an advanced course for RF engineers, signal analysts and technical professionals who want to apply machine learning to signal analysis at scale. Existing RF and signal-analysis knowledge is expected. It is also suitable for experienced data scientists moving into RF who are comfortable with machine-learning concepts but need to understand how they apply to IQ data and real-world signals.

Not sure where you fit? Find out more about our training audience and prerequisites.

Required equipment, tools, and software

You will need a laptop with at least 8GB RAM, preferably 16GB, an up-to-date and stable operating system, and an Ethernet port or reliable Ethernet dongle. Your laptop must be fully under your control so that you can install tools and dependencies and run arbitrary code.

It is useful to have the ability to run virtual machines. Any VMs provided during the course will be suitable for importing into VirtualBox; if you use a different hypervisor, you should be comfortable importing VMs from OVF file formats.

The SPYR synthetic range and labelled IQ datasets used throughout the practical exercises will be provided. No specialist RF hardware is required.

We put together a goodie bag which directly relates to the activities in the course. The free goodie bag is yours to keep at the end of the course, just make sure you have enough luggage space to take it home. The exact details of what is in it will vary depending on availability but we always make it a useful and interesting collection. We provide any other tools needed to complete all the tasks set, aside from a laptop.

You don’t need to bring anything extra other than your enthusiasm!

Venue and travel information

The classroom

The classroom is well appointed, has good WiFi and hot and cold drinks, it’s spacious, comfortable, has plenty of power sockets, lots of natural light, and is wheelchair friendly. The course is delivered in English and digital versions of slides and handouts will be provided where appropriate.

Food and refreshments

Lunch and morning and afternoon snacks are provided so please make sure you let us know about any dietary needs at least a week before we get started. With the exception of the social night, all other meals are for you to organise. We suggest getting a hotel that provides breakfast, and there are many good restaurants in Manchester for your evening meals. We will try and facilitate additional social arrangements, but, this is down to the individuals present.

Staying in Manchester

There are a number of good and affordable hotels in the Manchester area. We are based in an area called Media City and we are in the same complex as the northern headquarters of the BBC. This means that there are lots of facilities locally and you could choose to not venture into the city centre.

Getting here

If you do choose to look further around there are good tram links that can take you into the city as well as to key travel hubs such as Piccadilly, the national railway station, and Manchester International Airport.

Please check the weather before you travel and bring suitable clothes for the season. If in doubt, assume you will need a waterproof coat and an umbrella. The locals will tell you that Manchester is one of the rainiest places in the world, its not actually true but it does drizzle more than you might expect, even in summer.

Ready to scale your RF analysis with machine learning?

Take established RF skills further with machine learning techniques designed for complex and high-volume signal analysis. Over three days, you’ll work with labelled IQ data to explore classification, fingerprinting, denoising and automated spectrum triage, testing your models against known ground truth.

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