Indoor Radio Map Dataset

Citation Author(s):
Stefanos
Bakirtzis
Çagkan
Yapar
Kehai
Qui
Ian
Wassell
Jie
Zhang
Submitted by:
Stefanos Bakirtzis
Last updated:
Thu, 08/29/2024 - 06:48
DOI:
10.21227/c0ec-cw74
Data Format:
License:
5
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Abstract 

Efficient and realistic tools capable of modeling radio signal propagation are an indispensable component for the effective operation of wireless communication networks. The advent of artificial intelligence (AI) has propelled the evolution of a new generation of signal modeling tools, leveraging deep learning (DL) models that learn to infer signal characteristics. This Grand Challenge will probe the potential of DL algorithms to infer wireless signal attenuation in indoor propagation environments, where modeling signal propagation is more challenging due to a substantially larger number of reflected, refracted, scattered, or diffracted electromagnetic field components. To this end, we release a large dataset comprising radio maps from ray tracing simulations conducted in indoor environments of varying complexity, multiple frequency bands, and assuming different antenna radiation patterns. Exploiting these data enables the development of full-fledged data-driven propagation models that can generalize simultaneously over new building layouts, frequency bands, and antenna radiation patterns, thus paving the way for the replacement of legacy radio signal propagation modeling techniques.

Instructions: 

The zip file includes (also see the documentation):

(i)  An "Input" folder containing an indicative input tensor for each simulation (normal incidence reflectance & transmittance for the geometry & distance)

(ii) An "Output" folder with the PL radio map for each simulation 

(iii) A "Position" folder including the Tx positions for all buildings, frequencies, and antennas

(iv) A "Radiation_Patterns" folder comprising the azimuth radiation pattern (for simplicity simulations assume no elevation) for each one of the 5 antennas (Ant1 is isotropic, so all gains are 0)

(v) A "Building_Details" folder including the geometry boundary (most useful info here is W, H, but one can also get these from the image size)

 

Naming Conventions: B(#ID)_Ant(#ID)_f(#ID)_S(#ID)  ~ for buildings (#ID) ranges from 0 to 25, for the antennas from 1 to 5, for frequencies from 1 to 3, and for samples from 0 to 49 (for Ant1) and 0 to 79 for (Ant2, Ant3, Ant4, and Ant5)

 

The script file includes indicative code snippets for:

(i) Reading a sample and getting the sample frequency. Using additional features related to frequency will be helpful for Tasks 2 & 3

(ii) Computing angles and extracting antenna gain; you can use this information to extract additional features for Task 3

(iii) Resizing data to a standard scale (note that downsampling substantially compared to the initial dimensions might distort the geometry)

(iv) Augmenting data by flipping. We advise further exploring data augmentation techniques for your work, e.g., additional rotations, cropping, etc.

(v) An indicative data loader in torch

 

Notes: 

1. If you use data augmentation techniques you can modify your loader accordingly or save the augmented data as new images

2. If you extract new features you can modify your loader accordingly (to perform additional operations) or save the new input tensors as new images. For instance, a 4-channel tensor can be saved as an RGBA image  with the same naming convention, or if you have more channels you can create images with the same name convention with two parts, P1 (channel 1 to 4) and P2 (channel 5 to 8), that you concatenate when you read the data.

 

Comments

  

Submitted by Stefanos Bakirtzis on Fri, 08/02/2024 - 20:46

Is there documentation detailing the contents of this dataset?

Submitted by Yaning Wang on Tue, 08/27/2024 - 08:05