Metadata-Version: 2.4
Name: handoverlab
Version: 1.8.0
Summary: System-level simulator of handover and mobility procedures in dense 5G and beyond-5G networks
Author: Mostafa Jalili Sanjarani, Yasser Elmi Sola
License-Expression: MIT
Project-URL: Homepage, https://github.com/mjs19971997/handoverlab
Project-URL: Issues, https://github.com/mjs19971997/handoverlab/issues
Project-URL: Documentation, https://mjs19971997.github.io/handoverlab/
Project-URL: Changelog, https://github.com/mjs19971997/handoverlab/blob/main/CHANGELOG.md
Keywords: handover,conditional handover,mobility,system-level simulation,5G,3GPP
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21
Requires-Dist: scipy>=1.7
Requires-Dist: pyyaml>=5.4
Requires-Dist: tomli>=1.1; python_version < "3.11"
Provides-Extra: test
Requires-Dist: pytest>=7; extra == "test"
Provides-Extra: cov
Requires-Dist: pytest>=7; extra == "cov"
Requires-Dist: pytest-cov>=4; extra == "cov"
Provides-Extra: lint
Requires-Dist: ruff>=0.6; extra == "lint"
Provides-Extra: parquet
Requires-Dist: pyarrow>=8; extra == "parquet"
Provides-Extra: report
Requires-Dist: matplotlib>=3.3; extra == "report"
Provides-Extra: gui
Requires-Dist: streamlit>=1.37; extra == "gui"
Requires-Dist: matplotlib>=3.3; extra == "gui"
Provides-Extra: docs
Requires-Dist: mkdocs>=1.6; extra == "docs"
Requires-Dist: mkdocstrings[python]>=0.26; extra == "docs"
Provides-Extra: dev
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Requires-Dist: ruff>=0.6; extra == "dev"
Requires-Dist: pyarrow>=8; extra == "dev"
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Requires-Dist: streamlit>=1.37; extra == "dev"
Dynamic: license-file

# HandoverLab

[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.23409131.svg)](https://doi.org/10.5281/zenodo.23409131) [![PyPI](https://img.shields.io/pypi/v/handoverlab.svg)](https://pypi.org/project/handoverlab/)

A system-level simulator for studying handover in dense 5G and beyond-5G networks, written in Python.

HandoverLab simulates 3GPP mobility procedures (baseline handover with Event A3, Release-16
conditional handover and DAPS handover, Release-18 L1/L2-triggered mobility) for many users moving through a street grid, an open area, along a highway, in an indoor office or across rural cells, with a
received-power-level radio model from TR 38.901, an L3-filtered measurement chain, a radio-link
failure model, and an outcome label for every handover attempt.

> **Status: 1.8.0** (`python -m pip install handoverlab`). The public surface (configuration and
> result formats with a schema version, the rule interface, metric names, commands and the
> documented Python names) follows the [stability policy](https://github.com/mjs19971997/handoverlab/blob/main/docs/stability.md): within 1.x it only grows,
> and anything removed is deprecated first. New models are options, so the results of earlier
> configurations do not change.

## See it

**Run and watch.** `handoverlab gui` opens a dashboard in the browser, on your computer only. Choose a
layout, users and rules, press Run, and follow the users. Each user is coloured by its serving cell:

![The dashboard map: users on the street grid, each joined to its serving site](https://raw.githubusercontent.com/mjs19971997/handoverlab/main/docs/images/dashboard.png)

**See where things happen.** The tab *Where* marks every handover, unnecessary handover and
failure at the place where it happened:

![Places of handovers and unnecessary handovers on the street grid](https://raw.githubusercontent.com/mjs19971997/handoverlab/main/docs/images/dashboard-where.png)

**Compare rules fairly.** All rules run on the same worlds, one world per seed. The tab *Compare*
shows each mean with its 95 % confidence interval over the seeds, and the paired difference to the
first rule:

![Handovers, unnecessary handovers, failures and rate of A3 and conditional handover, with confidence intervals](https://raw.githubusercontent.com/mjs19971997/handoverlab/main/docs/images/dashboard-compare.png)

**Run whole studies.** `handoverlab study` tunes every rule, checks gates, runs paired tests with
multiple-comparison correction and writes an HTML report:

![Study report: paired differences to a reference rule with bootstrap intervals](https://raw.githubusercontent.com/mjs19971997/handoverlab/main/docs/images/report-comparison.png)

## What it models

- **Scenarios**: the TR 37.885 urban street grid (configurable number of blocks; 433 m x 250 m
  pitch, 20 m streets) with sites on the pavement, an open area with a hexagonal site grid, a
  highway, the TR 38.901 indoor office, rural macro cells, explicit site lists, and built-in
  scenario cards (`handoverlab scenarios`).
- **Channel**: TR 38.901 UMi street canyon, UMa, RMa and InH-Office path loss (LOS/NLOS); line of sight from the
  building geometry plus the spatially consistent LOS state, hard or soft; spatially correlated shadowing; a
  time-varying component; L1 measurement error; beam gain and load-coupled interference;
  optionally a penetration loss per user (in-car loss of TR 38.901).
- **Mobility**: street-following users with turn probabilities, random waypoints in the open area,
  pedestrian and vehicular speed profiles or your own speed groups.
- **Traffic**: constant service rates per 5QI class, or a traffic model per user: full buffer,
  periodic packets (as VoIP) or FTP model 3 of TR 36.814. The load of every cell, and so the
  interference, follows the traffic of its users.
- **Measurements**: L1 samples, the TS 38.331 L3 filter, Event A3 with time-to-trigger, periodic
  reporting for conditional handover.
- **Handover procedures**: baseline handover (report, preparation, command, execution), Rel-16
  conditional handover (up to 8 prepared candidates) and DAPS handover, Rel-18 L1/L2-triggered
  mobility and conditional LTM from the Rel-19 work, behind a small rule interface. Rules that learn from outcomes are supported, among them
  mobility robustness optimisation of a cell individual offset per cell pair (`a3_mro`).
- **Failure model**: Qout/Qin with windows, N310/N311/T310, lost commands and configurations,
  execution failures, re-establishment (TR 36.839 / TR 36.842 values by default), with options for
  the link monitoring (own interferer load, restart on a new cell, ideal random access).
- **Metrics**: outcome of every attempt (useful, unnecessary, too early, wrong cell, too late,
  censored), failures by cause, ping-pong, preparation overhead, interruption, delivered rate,
  signalling (RRC messages, commands), L1 measurements of the device and network energy (EARTH
  model).
- **Reproducibility**: one world per seed shared by all compared rules (paired comparison); the
  engine draws no random numbers; golden event-log fingerprints guard every change.

## Built for beyond-5G

HandoverLab targets the questions of dense beyond-5G networks: small cells 50-300 m apart, the
upper mid-band (FR3, 7-24 GHz; the scenario `fr3-urban-15ghz`, 10 GHz by default), and the
mobility procedures beyond baseline handover: conditional handover, DAPS handover,
L1/L2-triggered mobility of Release 18 and conditional LTM from the Release 19 work, next to rules that learn from handover outcomes and
A3 tuned by mobility robustness optimisation, the fair baseline for a new rule. The cost
of each procedure (signalling, prepared cells, interruption, energy) is measured next to its
benefit.

## Calibrated against 3GPP

The radio model passes the large-scale calibration of TR 38.901 (clause 7.8.1): in the
calibration drop of 3GPP, the CDFs of the coupling loss and of the geometry (SINR) lie inside the
range of the 21 companies that reported their results in the 3GPP contribution R1-165974 (Huawei,
Ericsson, Nokia, Samsung, Qualcomm and others), for UMa, UMi and the indoor office at 6 and
30 GHz. 325 of 342 compared percentiles are inside the range, none is more than about 1 dB from
the industry mean, and `handoverlab verify` repeats the check
([details](https://github.com/mjs19971997/handoverlab/blob/main/docs/calibration-3gpp.md)).

![HandoverLab against the 21 companies of 3GPP R1-165974](https://raw.githubusercontent.com/mjs19971997/handoverlab/main/docs/images/calibration_38901.png)

## Checked against ns-3

The same 24 Event-A3 settings were simulated in HandoverLab and in the packet-level simulator
ns-3 with 5G-LENA. Handover and unnecessary-handover rates agree (median ratio 0.92, rank
correlation 0.99). For failure rates, with the in-car loss of ns-3 and the restart of the link
monitoring switched on, the order of settings and conditions agrees too, also on ns-3 results
that were held back from any tuning (rank correlation 0.82-0.91). The absolute values
differ, so compare failure rates between rules within HandoverLab. The causes are traced in
[docs/calibration.md](https://github.com/mjs19971997/handoverlab/blob/main/docs/calibration.md). On two of them HandoverLab follows the 3GPP texts more closely
than ns-3:

- **In-car penetration loss.** TR 38.901 clause 7.4.3.2 gives it to a user inside a car. ns-3 adds
  it only while a user moves at 30-120 km/h and drops it above 120 km/h. HandoverLab applies it
  per user when configured (`radio.penetration_db`), at any speed.
- **Time to detect a radio problem.** TS 38.133 clause 8.1 evaluates out-of-sync over about
  200 ms. ns-3 counts control receptions instead of time, and at light traffic this took 0.65 to
  0.83 s in the reference runs. HandoverLab evaluates over time (`protocol.qout_window_s`, 0.2 s).

On a third point ns-3 was closer to the 3GPP texts: link monitoring restarts on a newly joined cell.
HandoverLab has had this as an option since 1.3.0 (`protocol.rlm_holdoff_s`). The comparison
covers baseline handover at 3.5 GHz only: 5G-LENA has no conditional handover, DAPS or
L1/L2-triggered mobility.

## What's new

- **1.8.0**: the radio model calibrated against 3GPP: the large-scale calibration of TR 38.901
  clause 7.8.1 against the 21 companies of R1-165974, as the `verify` group `calibration`.
- **1.7.0**: indoor office (TR 38.901 InH) and rural (RMa) scenarios with the IMT-2020 link
  budgets, the optional soft LOS state of TR 38.901, and the rule `a3_mro` (mobility robustness
  optimisation of a cell individual offset per cell pair).
- **1.6.0**: conditional LTM (rule `cltm`), from the Release 19 work on L1/L2-triggered mobility.
- **1.5.0**: signalling and energy metrics (RRC messages, handover commands, L1 measurements,
  site power and energy per bit); why failure rates differ from ns-3, stated in the README.
- **1.4.0**: traffic models per user (`traffic.model`: full buffer, periodic, FTP model 3); a
  dashboard with confidence intervals, a map of where handovers and failures happen, and the status
  of a running study; the ns-3 link-monitoring finding in the calibration report.
- **1.3.0**: optional restart of the link monitoring on a newly joined cell (TS 38.133).
- **1.2.0**: optional penetration loss per user; the ns-3 comparison with the in-car loss of ns-3.
- **1.1.0 / 1.1.1**: optional link-monitoring model; held-back ns-3 results; issue forms, CI on
  Windows for every test.

Earlier versions and every detail: the [changelog](https://github.com/mjs19971997/handoverlab/blob/main/CHANGELOG.md).

## Install

HandoverLab needs Python 3.9 or later. NumPy, SciPy and PyYAML are installed with it; matplotlib
(reports, extra `report`), pyarrow (Parquet tables, extra `parquet`), Streamlit (the dashboard,
extra `gui`) and MkDocs (the documentation site, extra `docs`) are optional.

From PyPI:

```bash
python -m pip install handoverlab                  # the core package
python -m pip install "handoverlab[report,gui]"    # with reports and the dashboard
handoverlab verify --only pathloss fingerprints    # optional: a quick check of the installation
```

From source (for development, the tests and the examples):

```bash
git clone https://github.com/mjs19971997/handoverlab.git
cd handoverlab
python -m pip install -e ".[test,report]"    # add gui for the dashboard: ".[test,report,gui]"
python -m pytest            # optional: run the tests (a few minutes)
handoverlab verify          # optional: verification report (about two minutes)
```

## Quick start

Write an example configuration and run it:

```bash
handoverlab init config.yaml
handoverlab run config.yaml -o results/metrics.csv
```

`run` writes one row per seed and rule with all metrics and the fingerprint of the event log, to
CSV or (with a `.json` output name) to JSON together with the resolved scenario and the package
versions. `handoverlab rules` lists the available rules and `handoverlab scenarios` the built-in
scenarios (`scenario: smoke` in a configuration uses one). Configurations can be YAML, TOML or JSON;
see [examples/](https://github.com/mjs19971997/handoverlab/tree/main/examples).

A whole study, with tuning, gates, paired tests and a report (a few minutes):

```bash
handoverlab study examples/study_smoke.yaml --workers 2 --report
```

The report lands in `results/study-smoke/report/` (`report.html`, `report.md`, figures);
re-running the command skips everything that is already done, and `handoverlab status
results/study-smoke` shows what is left and how long it will take.

Or explore in the browser:

```bash
python -m pip install "handoverlab[gui]"
handoverlab gui                      # http://localhost:8501, on your computer only
```

## Example

```python
import handoverlab as hl

scenario = hl.Scenario.from_dict({
    "layout": {"kind": "street_grid", "blocks": [3, 3], "site_spacing_m": 150.0},
    "mobility": {"users": 20, "profile": "veh16"},
    "run": {"duration_s": 60.0, "warmup_s": 10.0},
})
rules = [
    {"rule": "a3", "name": "A3", "off_db": 3.0, "ttt_s": 0.16},
    {"rule": "cho", "name": "CHO", "o_prep_db": -2.0, "o_exec_db": 3.0, "ttt_exec_s": 0.08},
]
for r in hl.simulate(scenario, rules, seeds=[1]):      # both rules run on the same world
    print(r.rule, r.metrics["handovers_pm"], r.metrics["unnecessary_pm"], r.metrics["failures_pm"])
```

Sample output of [examples/traffic_models.py](https://github.com/mjs19971997/handoverlab/blob/main/examples/traffic_models.py), the same rule under the four
traffic models (one seed, 30 users, 60 s on the urban grid at 10 GHz):

```text
model        cell load  rate [Mbit/s]  delivered  SINR [dB]  failures/UE-min
fixed             0.28          14.25       0.98       24.8             0.00
full_buffer       0.51         707.08       0.48       22.4             0.04
periodic          0.28           0.04       0.96       24.9             0.00
ftp3              0.28           3.93       0.93       24.9             0.00
```

Busier cells interfere more: under full buffer the SINR drops by more than 2 dB and failures
appear.

The full script is [examples/compare_a3_cho.py](https://github.com/mjs19971997/handoverlab/blob/main/examples/compare_a3_cho.py). Writing your own
handover rule takes a few lines; see the [user guide](https://github.com/mjs19971997/handoverlab/blob/main/docs/user_guide.md).

## Documentation

The documentation site is at **<https://mjs19971997.github.io/handoverlab/>**. It is a MkDocs site (`python -m pip install -e ".[docs]"`, then `mkdocs serve`);
the pages are plain Markdown in [docs/](https://github.com/mjs19971997/handoverlab/tree/main/docs) and readable on GitHub as well.

- Tutorials: [your first run](https://github.com/mjs19971997/handoverlab/blob/main/docs/tutorials/first-run.md),
  [writing your own rule](https://github.com/mjs19971997/handoverlab/blob/main/docs/tutorials/own-rule.md),
  [a fair tuning study](https://github.com/mjs19971997/handoverlab/blob/main/docs/tutorials/tuning-study.md),
  [reading the report](https://github.com/mjs19971997/handoverlab/blob/main/docs/tutorials/reading-the-report.md).
- [User guide](https://github.com/mjs19971997/handoverlab/blob/main/docs/user_guide.md): concepts, scenarios, configuration keys, command line, Python
  API, rule plug-ins and their contract checks, event logs and their levels, verification, metrics,
  studies (tuning, gates, storage, workers, status, reports), paired statistics, limitations.
- How-to guides: [dashboard](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/dashboard.md), [add a scenario](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/add-scenario.md),
  [run on many cores](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/many-cores.md), [export event logs](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/export-event-logs.md),
  [reproduce and check results](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/reproduce.md), [DAPS and LTM](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/daps-ltm.md),
  [a learning-based rule](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/learning-rule.md), [the upper mid-band](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/upper-mid-band.md),
  [a highway](https://github.com/mjs19971997/handoverlab/blob/main/docs/guides/highway.md).
- Reference: [command line](https://github.com/mjs19971997/handoverlab/blob/main/docs/reference/cli.md), [metric names](https://github.com/mjs19971997/handoverlab/blob/main/docs/reference/metrics.md) and the
  API (generated from the docstrings, in the site); [stability policy](https://github.com/mjs19971997/handoverlab/blob/main/docs/stability.md).
- [Scenario cards](https://github.com/mjs19971997/handoverlab/tree/main/docs/scenarios): the built-in scenarios with the source of every parameter.
- [Verification](https://github.com/mjs19971997/handoverlab/blob/main/docs/verification.md), [failure-model calibration](https://github.com/mjs19971997/handoverlab/blob/main/docs/calibration.md) and
  [performance](https://github.com/mjs19971997/handoverlab/blob/main/docs/performance.md).
- [Changelog](https://github.com/mjs19971997/handoverlab/blob/main/CHANGELOG.md), [contribution guide](https://github.com/mjs19971997/handoverlab/blob/main/CONTRIBUTING.md) and [releasing](https://github.com/mjs19971997/handoverlab/blob/main/docs/RELEASING.md).

## Roadmap

1. Stage 1 (0.1.0a1): core simulator, configuration, rule interface, command line, tests.
2. Stage 2 (0.2.0a1): verification in depth (invariant checks, golden event-log fingerprints, radio
   statistics, rule contract test kit, wider bh6 regression, ns-3 reference case), event logs as
   tables, the `verify` command, scenario cards, a faster world build.
3. Stage 3 (0.3.0a1): studies: equal-budget tuning with automatic gates, the selection rule, paired
   statistics, job records and Parquet tables, resumable studies with reused runs, worker
   processes, `study` and `report` commands, batched engines.
4. Stage 4 (0.4.0a1): browser dashboard (extra `gui`), tutorials, API and command-line
   reference with a documentation site, event-log levels, `status` with the time left, the
   grid-edge policy `extend`, failure-model calibration against ns-3 / 5G-LENA.
5. Stage 5 (1.0.0rc1): the public interfaces frozen and documented with a
   schema version (configuration, results, rule interface, metric names) and a stability policy;
   how-to guides; a lint job and a coverage threshold in CI; a nightly comparison of fingerprints
   between Linux and Windows; release plumbing for PyPI and Zenodo.
6. Stage 6 (1.0.0rc2): DAPS handover, L1/L2-triggered mobility, a learning-based
   example rule and an upper mid-band scenario, all additive.
7. Stage 7 (1.0.0rc3): a highway layout with lane-keeping vehicular mobility and its scenario
   card, site lists from CSV files, all additive.
8. Stage 8 (1.0.0rc4): the 3GPP citations and the documentation checked and
   corrected; no change of results.
9. 1.0.0: the first stable release, published on PyPI.
10. 1.0.1: the dashboard image shows on the PyPI project page; no change of results.
11. 1.0.2: README links work on PyPI, the documentation site is linked, text corrections; no change
    of results.
12. 1.1.0-1.3.0: optional link-monitoring and penetration-loss models traced against ns-3; issue
    forms and CI for every test on Windows.
13. 1.4.0: traffic models per user, a richer dashboard, pictures in the README.
14. 1.5.0: signalling and energy metrics.
15. 1.6.0: conditional LTM.
16. 1.7.0: indoor and rural scenarios; mobility robustness optimisation.
17. **1.8.0 (this release)**: the radio model calibrated against 3GPP (TR 38.901 clause 7.8.1).
18. Later (planned, not done): optional compiled kernels for the step loop.

## Citation

If you use HandoverLab, please cite it as described in [CITATION.cff](https://github.com/mjs19971997/handoverlab/blob/main/CITATION.cff) (GitHub shows
it under *Cite this repository*). The releases are archived on Zenodo:
[doi:10.5281/zenodo.23409131](https://doi.org/10.5281/zenodo.23409131) cites all versions (it resolves to
the latest); version 1.0.0 is [doi:10.5281/zenodo.23409132](https://doi.org/10.5281/zenodo.23409132)
and version 1.0.1 [doi:10.5281/zenodo.23409402](https://doi.org/10.5281/zenodo.23409402).

## Licence

MIT. See [LICENSE](https://github.com/mjs19971997/handoverlab/blob/main/LICENSE).

## Authors

Mostafa Jalili Sanjarani and Yasser Elmi Sola
