comnum-sim 0.3.0

Monte Carlo engine of comnum: parameter grids, scenarios, parallel runner independent of the thread count, stopping rules, confidence intervals
Documentation

comnum-sim

crates.io docs.rs

Part of comnum, link-level simulation of digital communication systems, wireless and optical. Layer 3 (simulation) of the workspace, whose dependencies point downward only (architecture).

The Monte Carlo engine: a scenario (one random trial of a link) run on every condition of a parameter grid, as many times as a stopping rule requires, on a thread pool, with every metric reported with a confidence interval. The results do not depend on the number of threads.

Most users depend on the facade comnum, which re-exports this crate as comnum::sim. The API is documented on docs.rs/comnum-sim, with the formula that every item implements.

Organisation

A scenario (one random trial of a link) runs on every condition of a grid, as many times as a stopping rule requires, in parallel on every core, and every metric is reported with a confidence interval. The organisation follows PyTorch Lightning:

PyTorch Lightning comnum::sim Role
LightningModule Scenario setup once per condition, trial many times
self.log(...) Record error counts (Wilson intervals) or values (normal intervals)
Trainer Runner batches of trials on a thread pool
seed_everything Runner::seed trial t of condition c draws from stream(seed, name, c, t)
max_epochs, EarlyStopping Stop max_trials, min_errors, precision, max_time, combined with or/and
Callback Callback ProgressBar, SkipAfter (skip the rest of a sweep below a BER floor)
Logger Logger CsvLogger (a row per condition as it completes), JsonLogger
fast_dev_run Runner::fast_dev_run one batch per condition

The records of a batch are merged in trial order, so the results are identical whatever the number of threads.

use comnum::prelude::*;
use comnum::sim::scenario::from_fn;

let qpsk = from_fn("qpsk", |cond, rng, rec| {
    let c = Constellation::qam(4)?.normalized();
    let bits = source::bits(2000, rng);
    let rx = awgn::awgn_ebn0(&c.map_bits(&bits)?, cond.f64("ebn0_db")?, 2.0, 1.0, rng);
    rec.errors("ber", metrics::bit_errors(&c.demap_bits(&rx), &bits)? as u64, 2000);
    Ok(())
});
let results = Runner::new()
    .seed(7)
    .stop(Stop::min_errors("ber", 500).or(Stop::max_trials(10_000)))
    .callback(ProgressBar::default())
    .callback(SkipAfter::below("ber", 1e-6, "ebn0_db"))
    .logger(CsvLogger::new("results/qpsk.csv"))
    .run(&qpsk, &Grid::new().axis("ebn0_db", [0.0, 2.0, 4.0, 6.0, 8.0]))?;

The Python package runs trials written in Python on the same runner (comnum.sim), with the same streams, statistics, stopping rules and loggers.

Licence

MIT or Apache-2.0, at your option (LICENSE-MIT, LICENSE-APACHE, shipped in the package).