Reuse a synthetic OFDM frame for range evidence

Transform a known OFDM frame into a synthetic delay profile and justify one sensing-airtime and payload operating point.

Radio Remi: predict first, inspect every intermediate value, and keep the claim inside the evidence., your practice guide

Radio Remi: predict first, inspect every intermediate value, and keep the claim inside the evidence.
Predict the reading, then compare it with the measurement.

Python 3 in your browser (JupyterLite)

Python · no install

Transform a known OFDM frame into a synthetic delay profile and justify one sensing-airtime and payload operating point.

Tier 2 · Web · paste-in setup · No account

Version tested: Python 3.12.7 / Pyodide 0.27.6 in JupyterLite 0.6.4; captureSource playwright:jupyterlite. Date: 2026-09-22.

Open the notebook in your browser and run each Python cell; no install or account is needed.

Three ways to run: use JupyterLite here with no install; run main.py locally from the downloadable lab folder; or open the same notebook in Google Colab.

Open in your browser (new tab)

Steps

Screens captured against JupyterLite Python 3.12.7 / Pyodide 0.27.6; Chromium 148.0.7778.96 on 2026-09-22; the tool may have moved on — the text steps are the contract.

  1. Step 1

    Do
    Predict what narrower occupied bandwidth will do to two echoes.
    You will see
    The known tones, frequency spacing, echo delay bins, and synthetic status print.
    Why it matters
    Wider occupied bandwidth improves the ability to separate nearby delays; longer coherent observation improves Doppler resolution but slows the result and demands more stable timing.

  2. Step 2

    Do
    Generate the known OFDM symbols and received values.
    You will see
    Two transmitted and two received complex values print.
    Why it matters
    An OFDM transmitter places known or decodable symbols $X[k]$ on many subcarriers.

  3. Step 3

    Do
    Divide received values by known non-zero symbols.
    You will see
    Two channel estimates and the reconstruction residual print.
    Why it matters
    Where $X[k]$ is known and non-zero, the channel estimate is $H[k]=Y[k]/X[k]$.

  4. Step 4

    Do
    Transform the channel estimate into a delay profile.
    You will see
    Two strongest bins, peak strengths, and round-trip range-bin spacing print.
    Why it matters
    An inverse Fourier transform across subcarriers turns frequency variation into a delay profile.

  5. Step 5

    Do
    Compare full and narrow occupied bandwidth.
    You will see
    Four quantitative resolution and peak-shape values print.
    Why it matters
    More bandwidth can separate closer reflectors, but spectrum availability, radio front ends, channel occupancy, and regulations constrain it.

  6. Step 6

    Do
    Vary sensing slots inside an eight-symbol schedule.
    You will see
    A four-row table reports the modeled payload fraction.
    Why it matters
    Data symbols, pilots, guard intervals, retransmissions, beams, and sensing bursts all compete for finite resources.

  7. Step 7

    Do
    Write a bounded operating-point result card.
    You will see
    The selected and rejected schedules print with untested field requirements.
    Why it matters
    Report payload throughput, packet delay, packet loss, range-bin spacing, update interval, false alarms, missed detections, energy, and compute load separately.

Chapter checks

These questions refer to the chapter’s examples. Use the return links to review their answers.

  1. Why must the receiver know or decode an OFDM symbol before using Y[k]/X[k] as a channel estimate?

    Return to the chapter’s knowledge check

  2. What proves a chosen ISAC schedule?

    Return to the chapter’s knowledge check

Return to Integrated Sensing and Communication for IoT · Browse Labs