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5 scoped briefs

3 mini · 2 major

Signal Processing & Wireless Communications

The input here is a waveform, and the answer is a curve checked against theory rather than a single accuracy number. Projects fail in two ways: a BER curve that does not sit on the analytical bound because the simulation is quietly wrong and nobody checked, or a folder of plots with no baseline to compare against. Everything on the SDR side is receive-only on public broadcast — never a radiating transmitter without a licence. Prerequisites, stated plainly: the two SDR briefs need an RTL-SDR dongle you buy, and the MATLAB briefs assume a campus-wide licence with the Communications Toolbox (the individual Student Suite does not bundle it). Projects 1 and 2 are fully doable in Python if MATLAB is unavailable; Project 4 is MATLAB-only and additionally needs the 5G Toolbox.

MATLABSignal Processing ToolboxCommunications ToolboxImage Processing ToolboxPython (NumPy / SciPy)GNU RadioRTL-SDRPyTorch
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The standard

Build the system. Understand every trade-off.

Every brief ends with working software, documented decisions, and evidence you can present—not a folder of code you cannot explain.

Two audiences, one engineering standard

Academic depth meets production discipline.

For students

Pick a brief below, or bring the problem statement your department handed you. We architect it with you, review every commit, deploy it to a real URL, and rehearse the viva until no question surprises you.

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For companies

PHY-layer simulation, SDR receiver chains, classical image-processing pipelines, and reproducing a published algorithm as code that actually runs.

Discuss a company build

Mini projects

Focused scope. Real engineering.

Three to four weeks. Narrow enough to finish, deep enough that you learn the decision that actually matters.

3 briefs
Mini project3–4 weeks

OFDM Link Simulation with BER-vs-SNR Curves

A full OFDM transmit and receive chain over AWGN, Rayleigh and Rician channels, with BER curves that land on their analytical references — and a cyclic-prefix sweep that shows exactly where they stop landing.

MATLABCommunications ToolboxPython (NumPy / SciPy)

You walk away with

  • BPSK / QPSK / 16-QAM / 64-QAM chain with IFFT, cyclic prefix and pilot insertion
  • BER-vs-SNR curves over AWGN, Rayleigh and Rician, each overlaid on its reference (Rician by MGF-based numerical evaluation — no closed form exists)
  • Cyclic-prefix sweep showing ISI once the CP is shorter than the channel delay spread
  • Zero-forcing vs. MMSE equaliser comparison with before/after constellations
  • Monte Carlo confidence note: bits per SNR point, minimum-error-count stopping rule, and the SNR beyond which the run has too few errors to report a BER
Mini project3–4 weeks

Classical Image Enhancement and Compression Pipeline

Histogram equalisation, morphological cleanup, edge detection and a DWT/DCT codec scored against JPEG — the hard part being that a transform is not a compressor, so there is no bitrate until you build the quantizer and the entropy coder.

MATLABImage Processing ToolboxPython (OpenCV / scikit-image)PyWavelets

You walk away with

  • Enhancement stage: global HE, CLAHE and gamma correction, each scored on the same image set
  • Segmentation stage: Otsu thresholding plus morphological opening/closing vs. region growing
  • DWT and DCT codecs with a deadzone quantizer and entropy coder (or an explicit first-order-entropy bit estimate, labelled as an estimate)
  • Rate-distortion curves (PSNR and SSIM vs. bpp) against JPEG, with JPEG bpp measured from actual encoded file size
  • 2AFC perceptual test (5 raters, 20 image pairs) reporting the agreement rate where PSNR and SSIM disagree
Mini project3–4 weeks

RTL-SDR FM Receiver and Spectrum Analyser

Pull real broadcast FM out of the air on a low-cost USB dongle, rebuild the whole demodulation chain from scratch in NumPy, and align it against the GNU Radio reference by cross-correlation — because two independent WBFM chains never match sample for sample.

RTL-SDRGNU RadioPython (NumPy / SciPy)pyrtlsdr

You walk away with

  • Working WBFM receiver in GNU Radio Companion, used as the reference
  • From-scratch NumPy chain (decimating FIR, quadrature demodulator, de-emphasis IIR, resample to 48 kHz)
  • Cross-correlation alignment against the reference: delay-compensated SNR ≥ 25 dB, max absolute deviation, and overlaid output spectra
  • FIR designed to a written spec (passband ripple, stopband attenuation, order) — not a dropped-in default block
  • PSD and waterfall plots at each decimation stage, including the aliasing from decimating before filtering
  • Sample-rate sweep with measured dropped-sample counts, identifying the stable maximum rate on your host (report what you measure, not the spec sheet)

Major projects

Capstones you can defend.

Ten to twelve weeks. Architecture, trade-offs, failure modes, deployment, and evidence—the project that carries an interview.

2 briefs
Major project12 weeks

Reproduce and Extend a MIMO-OFDM Channel Estimation Paper

Rebuild a recent IEEE channel-estimation paper from its text until your curves match theirs, extend it to a channel the authors never tested, and write up honestly which figures you could not reproduce.

MATLABCommunications Toolbox5G ToolboxLaTeX (IEEEtran)

You walk away with

  • LS and MMSE estimators implemented from the paper's equations, not a toolbox one-liner
  • MSE-vs-SNR and BER-vs-SNR curves reproduced figure-by-figure against the published plots
  • Reproduction table: every figure marked matched / partially matched / not reproduced, each with a stated reason
  • An extension the paper does not cover (a 3GPP TDL profile, a pilot-density sweep, or a Doppler they held fixed)
  • IEEEtran write-up stating the extension as the contribution and the failures as failures
Major project10–12 weeks

Automatic Modulation Classification: Cumulant Baseline vs. CNN

Classify modulation from raw I/Q with a higher-order-cumulant classifier and a CNN on RadioML, then test both on the only two classes a student can legally receive off-air — WBFM and AM broadcast — because the dataset's synthetic impairments are not the ones the air puts there.

Python (NumPy / SciPy)scikit-learnPyTorchRTL-SDRGNU Radio

You walk away with

  • Cumulant classifier on RadioML 2018.01A (C40, C42, C63 into an SVM), with the feature maths derived rather than copied — 2016.10a as the fallback if compute is short
  • CNN over raw I/Q as the learned counterpart, on a matched train/validation/test split
  • Accuracy-vs-SNR curves for both classifiers plus confusion matrices at fixed SNR (no single headline accuracy number)
  • Live receive-only test scoped to WBFM and AM-DSB broadcast — the only two classes with legal off-air ground truth
  • Domain-gap study on the remaining classes: carrier-frequency and sample-rate offset injected into the RadioML test set

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