Getting Started

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"""# Getting Started with DeepMIMO.

[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/1_getting_started.py)
 
[![GitHub](https://img.shields.io/badge/Open_on-GitHub-181717?logo=github&style=for-the-badge)](https://github.com/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/1_getting_started.py)

---

**Tutorial Overview:**
- **Part 1**: Hello World - Load a simple scenario, generate channels
- **Part 2**: Deep Dive - Explore complex scenarios with multiple base stations
- **Part 3**: Discovery - Learn how to discover more using `dm.info()`,
  aliases, and implicit computations

**Related Video:** [Getting Started Video](https://youtu.be/LDG6IPEHY54)

---
"""

"""# Getting Started with DeepMIMO. [](https://colab.research.google.com/github/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/1_getting_started.py)   [](https://github.com/DeepMIMO/DeepMIMO/blob/main/docs/tutorials/1_getting_started.py) --- **Tutorial Overview:** - **Part 1**: Hello World - Load a simple scenario, generate channels - **Part 2**: Deep Dive - Explore complex scenarios with multiple base stations - **Part 3**: Discovery - Learn how to discover more using `dm.info()`, aliases, and implicit computations **Related Video:** [Getting Started Video](https://youtu.be/LDG6IPEHY54) --- """

Part 1: Hello World¶

Let's start with the absolute basics: installing DeepMIMO, loading a simple scenario, and generating channels.

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# Install DeepMIMO (if not already installed)
%pip install --pre deepmimo

# Install DeepMIMO (if not already installed) %pip install --pre deepmimo

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# Import necessary libraries
import numpy as np

import deepmimo as dm

# Import necessary libraries import numpy as np import deepmimo as dm

Load a Simple Scenario¶

We'll use the ASU Campus scenario, which is a simple outdoor scenario.

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# Download and load a scenario
scen_name = "asu_campus_3p5"
dm.download(scen_name)
dataset = dm.load(scen_name)

# Download and load a scenario scen_name = "asu_campus_3p5" dm.download(scen_name) dataset = dm.load(scen_name)

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# Check what we loaded
print(f"Loaded scenario: {scen_name}")
print(f"Dataset type: {type(dataset)}")
print(dataset)

# Check what we loaded print(f"Loaded scenario: {scen_name}") print(f"Dataset type: {type(dataset)}") print(dataset)

Generate Channels¶

Now let's generate some basic channel matrices.

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# Generate time-domain channels
ch_params = dm.ChannelParameters()
ch_params.freq_domain = False
dataset.compute_channels(ch_params)
channels = dataset.channel
print(f"Channel shape: {channels.shape}")
print(f"Channel type: {type(channels)}")

# Generate time-domain channels ch_params = dm.ChannelParameters() ch_params.freq_domain = False dataset.compute_channels(ch_params) channels = dataset.channel print(f"Channel shape: {channels.shape}") print(f"Channel type: {type(channels)}")

Basic Inspection¶

Let's check some basic properties of our dataset.

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# Check line-of-sight status
print(f"LOS status shape: {dataset.los.shape}")
print(f"Number of users with LOS: {np.sum(dataset.los == 1)}")
print(f"Number of users with NLOS: {np.sum(dataset.los == 0)}")
print(f"Number of users with no paths: {np.sum(dataset.los == -1)}")

# Check line-of-sight status print(f"LOS status shape: {dataset.los.shape}") print(f"Number of users with LOS: {np.sum(dataset.los == 1)}") print(f"Number of users with NLOS: {np.sum(dataset.los == 0)}") print(f"Number of users with no paths: {np.sum(dataset.los == -1)}")

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# Check pathloss
pathloss = dataset.pathloss
print(f"Pathloss shape: {pathloss.shape}")
print(f"Average pathloss: {np.mean(pathloss[pathloss > -np.inf]):.2f} dB")

# Check pathloss pathloss = dataset.pathloss print(f"Pathloss shape: {pathloss.shape}") print(f"Average pathloss: {np.mean(pathloss[pathloss > -np.inf]):.2f} dB")

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Part 2: Deep Dive with Complex Scenarios¶

Now let's explore a more complex scenario with multiple base stations and dive deeper into the dataset structure.

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# Load a city scenario with multiple base stations
city_scen = "city_18_denver_3p5"
dm.download(city_scen)
# Load specific TX/RX sets to get a single dataset
city_dataset = dm.load(city_scen, tx_sets=[1], rx_sets=[0])

# Load a city scenario with multiple base stations city_scen = "city_18_denver_3p5" dm.download(city_scen) # Load specific TX/RX sets to get a single dataset city_dataset = dm.load(city_scen, tx_sets=[1], rx_sets=[0])

Explore TX/RX Pairs and Sets¶

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# Get information about transmitter and receiver sets
txrx_sets = dm.get_txrx_sets(city_scen)
print("Available TX-RX sets:")
for txrx_set in txrx_sets:
    print(f"  {txrx_set}")

# Get information about transmitter and receiver sets txrx_sets = dm.get_txrx_sets(city_scen) print("Available TX-RX sets:") for txrx_set in txrx_sets: print(f" {txrx_set}")

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# Get TX/RX pair information
pairs = dm.get_txrx_pairs(txrx_sets)
print(f"Number of TX/RX pairs: {len(pairs)}")
print("First few pairs:")
for pair in pairs[:5]:
    print(f"  {pair}")

# Get TX/RX pair information pairs = dm.get_txrx_pairs(txrx_sets) print(f"Number of TX/RX pairs: {len(pairs)}") print("First few pairs:") for pair in pairs[:5]: print(f" {pair}")

Explore Available Matrices¶

Let's see what matrices/parameters are available in the dataset.

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# List available fundamental matrices
print("Available dataset attributes:")
for attr in dir(city_dataset):
    if not attr.startswith("_") and not callable(getattr(city_dataset, attr)):
        print(f"  - {attr}")

# List available fundamental matrices print("Available dataset attributes:") for attr in dir(city_dataset): if not attr.startswith("_") and not callable(getattr(city_dataset, attr)): print(f" - {attr}")

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# Explore some key matrices
print(f"Power matrix shape: {city_dataset.power.shape}")
print(f"Phase matrix shape: {city_dataset.phase.shape}")
print(f"Delay matrix shape: {city_dataset.delay.shape}")
print(f"AOA (azimuth) matrix shape: {city_dataset.aoa_az.shape}")
print(f"AOD (azimuth) matrix shape: {city_dataset.aod_az.shape}")

# Explore some key matrices print(f"Power matrix shape: {city_dataset.power.shape}") print(f"Phase matrix shape: {city_dataset.phase.shape}") print(f"Delay matrix shape: {city_dataset.delay.shape}") print(f"AOA (azimuth) matrix shape: {city_dataset.aoa_az.shape}") print(f"AOD (azimuth) matrix shape: {city_dataset.aod_az.shape}")

Scenario Summary¶

Get a high-level overview of the scenario.

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# Get scenario summary
print("Scenario Summary:")
dm.summary(city_scen)

# Get scenario summary print("Scenario Summary:") dm.summary(city_scen)

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Part 3: Discovery - Learning More¶

DeepMIMO provides powerful tools to help you discover and understand the available functions and parameters.

Using dm.info() for Parameter Documentation¶

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# Get information about all available parameters
dm.info("all")

# Get information about all available parameters dm.info("all")

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# Get information about a specific parameter
dm.info("power")

# Get information about a specific parameter dm.info("power")

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# Get information about channel parameters
dm.info("ch_params")

# Get information about channel parameters dm.info("ch_params")

Inspecting Function Docstrings¶

You can use dm.info() to inspect the docstrings of any function or object!

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# Inspect the docstring of a function
# dm.info(dm.load)  # Uncomment to view docstring
print("Use dm.info(dm.load) to view the docstring of dm.load()")

# Inspect the docstring of a function # dm.info(dm.load) # Uncomment to view docstring print("Use dm.info(dm.load) to view the docstring of dm.load()")

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# Inspect the docstring of a method
# dm.info(dataset.compute_channels)  # Uncomment to view docstring
print("Use dm.info(dataset.compute_channels) to view the docstring")

# Inspect the docstring of a method # dm.info(dataset.compute_channels) # Uncomment to view docstring print("Use dm.info(dataset.compute_channels) to view the docstring")

Understanding Aliases¶

DeepMIMO provides convenient aliases for common parameters.

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# Check if an alias resolves to a parameter
dm.info("rx_pos")  # This is an alias

# Check if an alias resolves to a parameter dm.info("rx_pos") # This is an alias

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# Common aliases
print("Using aliases:")
print("  dataset.user_pos (alias) -> dataset.rx_pos (actual)")
print("  dataset.bs_pos (alias) -> dataset.tx_pos (actual)")
print(f"  RX positions shape: {dataset.rx_pos.shape}")

# Common aliases print("Using aliases:") print(" dataset.user_pos (alias) -> dataset.rx_pos (actual)") print(" dataset.bs_pos (alias) -> dataset.tx_pos (actual)") print(f" RX positions shape: {dataset.rx_pos.shape}")

Implicit Computations¶

Some dataset attributes are computed on-the-fly when you access them.

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# These are computed implicitly:
print(f"Number of paths per user: {dataset.num_paths[:10]}")  # Computed from power/delay
print(f"Pathloss (dB) for first 5 users: {dataset.pathloss[:5]}")  # Computed from power
print(f"Distance (m) for first 5 users: {dataset.dist[:5]}")  # Computed from delay

# These are computed implicitly: print(f"Number of paths per user: {dataset.num_paths[:10]}") # Computed from power/delay print(f"Pathloss (dB) for first 5 users: {dataset.pathloss[:5]}") # Computed from power print(f"Distance (m) for first 5 users: {dataset.dist[:5]}") # Computed from delay

Discovering More Functions¶

Explore the available functions in the DeepMIMO module.

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# List key functions in the dm module
print("Key DeepMIMO functions:")
key_functions = [f for f in dir(dm) if not f.startswith("_") and callable(getattr(dm, f))]
for func in sorted(key_functions)[:20]:  # Show first 20
    print(f"  - dm.{func}()")

# List key functions in the dm module print("Key DeepMIMO functions:") key_functions = [f for f in dir(dm) if not f.startswith("_") and callable(getattr(dm, f))] for func in sorted(key_functions)[:20]: # Show first 20 print(f" - dm.{func}()")

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# Get help for any function
# dm.info(dm.download)  # Uncomment to view docstring
print("Use dm.info(dm.download) to view the docstring of any function")

# Get help for any function # dm.info(dm.download) # Uncomment to view docstring print("Use dm.info(dm.download) to view the docstring of any function")

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Next Steps¶

Now that you understand the basics, explore these tutorials:

• Tutorial 2: Visualization and Scene - Learn how to visualize coverage maps, rays, and 3D scenes
• Tutorial 3: Detailed Channel Generation - Deep dive into channel generation parameters
• Tutorial 4: User Selection and Dataset Manipulation - Learn how to filter and sample users
• Tutorial 5: Doppler and Mobility - Add time-varying effects to your channels
• Tutorial 6: Beamforming - Implement beamforming and spatial processing
• Tutorial 7: Convert & Upload Ray-tracing dataset - Work with external ray tracers
• Tutorial 8: Migration Guide - Migrating from DeepMIMO v3 to v4