OFDM with Adaptive ICI-Aware Equalization for Doubly Dispersive Channels

Preprint + reproducible simulation code for a like-for-like comparison of OFDM and OTFS on doubly dispersive (high-mobility) channels.

The inter-carrier-interference (ICI) floor that doubly dispersive channels impose on OFDM can be removed by a banded, multi-tap frequency-domain equalizer whose order is set adaptively from the delay–Doppler channel estimate. The band follows the Doppler, and the coefficients follow the fast fading. Under identical coding and interleaving, this receiver comes within about 1 dB of a delay–Doppler OTFS detector at roughly two orders of magnitude lower equalizer complexity. A matched-complexity study and an FDE-OTFS experiment show that OTFS's edge comes from its joint equalizer, not from the modulation-level spreading, and that the same diversity is recoverable by channel coding over OFDM.

📄 Paper: paper/main.pdf

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Repository layout

ofdm_vs_otfs/
├── paper/               compiled preprint (PDF) and the four figures
│   ├── main.pdf
│   ├── cases_jakes.png      (Fig. 1)
│   ├── ber_eps03.png        (Fig. 2a)
│   ├── ber_eps12.png        (Fig. 2b)
│   └── pulse_shaping.png    (Fig. 3)
└── code/                Python simulation suite (numpy / scipy / matplotlib)
    ├── channel.py           doubly-selective + Jakes/3GPP-TDL channels, AWGN
    ├── mmse.py              bias-corrected LMMSE (unbiased estimate + per-symbol SINR)
    ├── ldpc.py              self-contained regular LDPC (BP) + QPSK soft LLRs
    ├── zpofdm.py            ZP-OFDM TX + banded ICI-aware equalizer
    ├── cpofdm.py            standard CP-OFDM + single-tap equalizer
    ├── zakotfs.py           OTFS TX + delay–Doppler LMMSE detector
    ├── compare_cases.py     matched-complexity precoding framework (+ Fig. 1)
    ├── run_waveforms.py     four-receiver comparison incl. FDE-OTFS (Fig. 2)
    ├── run_pulse.py         pulse shaping / spectral mask (Fig. 3)
    ├── run_ber.py           domain-specific BER sweep (imperfect CSI, conv/LDPC)
    ├── run_techniques.py    waveform-domain 3-technique BER sweep
    ├── qam.py, coding.py    QAM + convolutional-code helpers (commpy)
    └── requirements.txt

Setup

cd code
python -m venv .venv && source .venv/bin/activate
pip install -r requirements.txt

Only numpy, scipy, and matplotlib are needed for the three paper figures. scikit-commpy is used only by run_ber.py --code cc.

Reproducing the figures

Run from code/. These are Monte-Carlo simulations with dense linear algebra, so each command takes on the order of 10–30 min on a laptop.

# Fig. 1: matched-complexity OFDM vs OTFS (Jakes/TDL, eps=0.2)
python compare_cases.py --frames 100 --snr 0 2 4 6 8 --out cases_jakes.png

# Fig. 2: four receivers on realistic 3GPP-TDL-C, mild and severe Doppler
python run_waveforms.py --frames 150 --eps_max 0.3 --snr 0 1 2 3 4 5 6 8 --out ber_eps03.png
python run_waveforms.py --frames 150 --eps_max 1.2 --snr 0 1 2 3 4 5 6 8 --out ber_eps12.png

# Fig. 3: pulse shaping / spectral containment
python run_pulse.py --frames 60 --out pulse_shaping.png

The compiled preprint is provided as paper/main.pdf. The commands above regenerate the four figures it contains (compare them against the paper/*.png in this repo).

Method notes

• Adaptive band. The equalizer half-width is Q = ceil(f_D / Δf) + Δ, read from the Doppler support of the delay–Doppler channel estimate. It sizes the band to the mobility while the coefficients track the fast fading.
• Bias-corrected soft decoding. All receivers use mmse.py: each equalizer output is de-biased and weighted by its own post-equalization SINR, so the full-channel LMMSE is a genuine lower bound on any banded/mismatched version.
• Matched-complexity comparison. compare_cases.py / run_waveforms.py write every waveform as a unitary precoding of the same circular time-domain signal (r = Gt · A · x), so OFDM, FDE-OTFS, and DD-OTFS differ only in the precoder and the equalizer band, an apples-to-apples cost/performance test.

Citation

@unpublished{dovelos_ofdm_ici_2026,
  author = {Konstantinos Dovelos},
  title  = {{OFDM} with Adaptive {ICI}-Aware Equalization for Doubly Dispersive Channels},
  note   = {Preprint},
  year   = {2026}
}

License

Released under the MIT License.