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# Zynq SDR Course **Engineering-grade SDR course: from mathematical model to measured RF signal** This site is the main course workspace. It connects theory, MATLAB/Simulink modeling, fixed-point DSP, FPGA implementation, AD9363 RF hardware, RTL-SDR reception, IQ recording and reproducible analysis.
MATLAB / Simulink Fixed-point DSP FPGA / HDL Zynq-7020 AD9363 RTL-SDR

SDR measurement loop

SDR measurement loop

Main idea

The course is not simulation-only. Every important model decision must eventually be connected to a hardware signal and verified through measured data.


Core engineering route

flowchart TB
    MODEL["1. Model and reference signal<br/>MATLAB / Simulink, ideal floating-point behavior"]
    FIXED["2. Fixed-point conversion<br/>word length, scaling, overflow and quantization noise"]
    FPGA["3. FPGA implementation<br/>streaming DSP blocks, latency, AXI interfaces"]
    RF["4. RF frontend<br/>AD9363 frequency plan, gain, filters and bandwidth"]
    CHANNEL["5. Physical channel<br/>coax with attenuation or controlled over-the-air path"]
    RX["6. Independent receiver<br/>RTL-SDR + HDSDR for external observation"]
    IQ["7. IQ recording<br/>WAV / RAW / CI16 with documented metadata"]
    METRICS["8. Engineering metrics<br/>FFT, SNR, EVM, BER and final conclusion"]

    MODEL --> FIXED --> FPGA --> RF --> CHANNEL --> RX --> IQ --> METRICS
    METRICS -. redesign algorithm .-> MODEL
    METRICS -. retune RF parameters .-> RF

What you will build

1. Signal model

Reference waveforms, sample-rate planning, modulation, filtering and expected spectra.

2. Fixed-point DSP

Scaling, quantization, coefficient precision, overflow control and hardware-oriented validation.

3. FPGA signal path

DDS/NCO, mixer, FIR, interpolation, AXI-Stream and real-time processing on Zynq.

4. RF measurement loop

AD9363 transmit path, external reception through RTL-SDR, HDSDR observation and IQ recording.


Course quality pack

These pages turn the repository into a more complete engineering course workspace:

Page Purpose
Course quality roadmap Defines the target level, checklist and backlog for course completion
Course status Gives the short engineering readiness board for the full course
Course readiness matrix Tracks whether each block is documented, runnable, plotted and measurable
Student path Gives the shortest reliable learner route through the material
Reviewer path Gives a fast evidence-oriented repository walkthrough
Instructor guide Explains how to use the repository as a teaching workspace
Hardware checklist Aggregates hardware readiness, RF safety, metadata and reporting links
Experiment manifests Connects selected labs with reproducible objectives and acceptance criteria
Hardware bring-up checklist Gives a repeatable procedure for board, RF path and receiver setup
SDR measurement report template Standardizes hardware/RF/IQ lab conclusions
Lab report template Gives a repeatable structure for each DSP/RF/FPGA lab report
IQ recording metadata guide Standardizes real-signal captures so measurements can be reproduced

Hardware baseline

RTL-SDR V3 Pro Xilinx Zynq-7020 with AD9363 module

IEEE-style generated figures

Lab 1 FFT Lab 3 constellation Lab 5 EVM Lab 6 BER

Learning tracks

Track Start here Engineering output
System view Model → FPGA → RF → Measurement End-to-end understanding of the SDR stand
Demo figures IEEE-style figures Reproducible plots and validation examples
Experiment manifests Manifest-driven labs Reproducible lab objectives and acceptance criteria
Hardware readiness Hardware checklist One-page route into bring-up, RF safety and measurement discipline
Reviewer route Reviewer path Fast maturity and evidence evaluation
Instructor route Instructor guide Classroom-oriented use of the repository
Course quality Course readiness matrix Transparent progress and engineering maturity
Russian course Русский обзор RU learning path and block navigation
English course English overview EN learning path and block navigation

Reproducibility

bash tools/reproduce_all.sh
mkdocs serve

The project is designed so that figures, documentation and the learning path can evolve together through GitHub Actions and MkDocs.