DeepMIMO v2 - Matlab and Octave

#### Table of Contents

**DeepMIMO ***v2* Features

*v2*Features

**Includes all features of DeepMIMO v1****Optimized**memory requirements and generation speedGenerates the channels between BSs and UEs

**Generates the channels between BSs and BSs (enabling integrated access-backhaul, RIS, etc.)****Allows for multiple antennas at both the BSs and UEs****Allows for changing panel orientations at the BS and UEs****Allows for applying receiver filtering for more accurate channel generation****Generates OFDM and time-domain channels****Outputs path parameters, path-loss, distances, among other possible outputs****Outputs transmitter/receiver locations****Available in Matlab, Octave, and Python**

## How Does it Work?

DeepMIMO v2 dataset generator processes the input ray-tracing file based on the parameters’ values specified in the DeepMIMO parameters file to generate the output dataset

Check the detailed documentation below for more information about the DeepMIMO v2 parameters and outputs

## Download and Installation

Step 1: (Generator scripts)

- Download DeepMIMO v2 MATLAB generator scripts.
- Extract the file
*DeepMIMOv2.zip*

Step 2: (Scenario)

- Select and download a scenario from the scenarios page.
- Extract scenario folder into the path
*DeepMIMOv2/Raytracing_scenarios/*

Step 3: (Parameter Configuration)

- Configure DeepMIMO parameters in
*parameters.m*file.

Step 4: (Data Generation)

- Edit and run
*DeepMIMO_Dataset_Generator.m*to configure and generate the dataset.

## Input Parameters

**scenario** *string*

* *

Name of the scenario to be loaded. To check and download available scenarios, please see the scenarios page.

**scene_first, scene_last** *integers*

* *

[For dynamic scenarios] Determines the range of dynamic scenario scenes to be loaded [scene_first, scene_last]

**active_BS** *integer array of active BSs*

The ID of the basestations to be included in the dataset. The basestation IDs can be selected from the scenario description. In the final dataset, the activated basestations IDs are renumbered in the same order starting from 1 to the number of active basestations.

**active_user_first, ****active_user_last** *integers*

** **

The range of rows are determined by these parameters.

Specifically, the row of users with the ID in the range [active_user_first, active_user_last] are selected.

**row_subsampling** *float in the range (0, 1]*

This parameter determines the ratio of the rows to be activated within the interval [active_user_first, active_user_last].

Speficially, it randomly samples round(row_subsampling*(active_user_last-active_user_first)) rows within the given interval.

The value 1 activates all the rows within the interval.

**user_subsampling ***float in the range (0,1)*

This parameter determines the ratio of the users to be activated within the active rows determined by the parameters user_subsampling, active_user_first and active_user_last. In each row, it allows random sampling of round(user_subsampling*number_of_users_in_row) users for activation.

The value 1 activates all the users within the active rows.

**num_ant_BS** *integer array of 3 dimensions, ***ant_spacing_BS ***float*

The basestation antenna parameters. num_ant represent the number of antenna elements in x-y-z dimensions, e.g., [1, 4, 2]. The antenna spacing between array elements is determined as (ant_spacing x wavelength).

An antenna array (UPA) of (num_ant(1) x num_ant(2) x num_ant(3)) elements is adopted for each active basestation.

The axes of the antennas match the axes of the ray-tracing scenario.

If there are multiple active antennas, a matrix of 3 dimensional panel sizes can be given as input. For example, if basestations 3 and 4 are activated, the num_ant can be set as [[1, 1, 1]; [1, 4, 1]], equipping basestation 3 with a single antenna and basestation 4 with a ULA of 4 antenna elements.

**num_ant_UE**** ***integer array of 3 dimensions,*** ant_spacing_UE ***float *

The UE antenna parameters. num_ant_UE represent the number of antenna elements in x-y-z dimensions, e.g., [1, 4, 2]. The antenna spacing between array elements is determined as (ant_spacing_UE x wavelength).

An antenna array (UPA) of (num_ant_UE(1) x num_ant_UE(2) x num_ant_UE(3)) elements is adopted for each active UE.

The axes of the antennas match the axes of the ray-tracing scenario.

**activate_array_rotation ***boolean*

This parameter activates the following BS and UE array rotation parameters. If it is set to 0, no rotation will be applied to any antenna array.

array_rotation_BS *float array of 3 dimensions*

* *

The BS antenna array rotation parameters, which consists of three rotation angles (in degrees). These angles rotate the BS antenna array in the given angles around the local x, y, z axes, respectively. To assign the same array rotation parameters to all active BSs, the following variable setting can be applied.

` ````
```params.array_rotation_BS = [x_rot, y_rot, z_rot]

*Alternative setting (different parameters for multiple BSs)*

To assign different array rotation parameters to each active BS, set an N x 3 matrix, with N being the number of active BSs. For instance, with two active BSs, the following variable setting can be applied.

` ````
```params.array_rotation_BS = [x_rot_1, y_rot_1, z_rot_1;
x_rot_2, y_rot_2, z_rot_2]

array_rotation_UE *float array of 3 dimensions*

* *

The UE antenna array rotation parameters, which consists of three rotation angles (in degrees). These angles rotate the UE antenna array in the given angles around the local x, y, z axes, respectively. To assign the same array rotation parameters to all UEs, the following variable setting can be applied.

` ````
```params.array_rotation_UE = [x_rot, y_rot, z_rot]

*Alternative setting (random parameters)*

To assign uniformly random array rotations to each UE, set a 3 x 2 matrix, where the first column defines the lower limits of the uniform distribution, and the second column defines the upper limits. For instance, for each UE, uniformly random values between the minimum and the maximum limits can be assigned for the array rotation angles as follows.

` ````
```params.array_rotation_UE = [x_rot_min, x_rot_max;
y_rot_min, y_rot_max;
y_rot_min, y_rot_max]

**enable_BS2BSchannels** *boolean*

Enable (1) or disable (0) generation of the channels between basestations

**radiation_pattern ***boolean*

(0) activate isotropic radiation patterns for each BS and user antenna element

(1) activate half-wave dipole radiation patterns for each BS and user antenna element

**bandwidth** *float*

Total bandwidth of the channel in GHz.

**activate_RX_filter** *boolean*

Activate (1) receive LPF for OFDM channels.

**generate_OFDM_channels** *boolean*

(0) activate time domain (TD) channel impulse response generation.

(1) activate frequency domain (FD) channel generation for OFDM systems.

**num_paths** *integer in [1, 25]*

Maximum number of paths to be considered (a value between 1 and 25), e.g., choose 1 if you are only interested in the strongest path

**num_OFDM** *integer*

Number of OFDM subcarriers (e.g., 256, 512, 1024)

**OFDM_limit ***integer*, **OFDM_sampling_factor** *integer*

The constructed channels will be calculated only at the sampled subcarriers to reduce the size of the dataset. The first OFDM_limit subcarriers are subsampled with OFDM_sampling_factor spacing between the selected subcarriers.

For OFDM_limit = 64 and OFDM_sampling_factor = 8, the subcarriers {1, 9, 17, 26, 33, …} are subsampled from the available subcarriers {1, 2, …, 64}.

**saveDataset** *boolean*

Autosaving option for the dataset.

(1) The generated dataset will be saved to the file ‘./DeepMIMO_dataset/DeepMIMO_dataset.mat’

(0) The generated dataset will only be returned but not saved.

## Output Parameters

#### Output Variables of Basestation i – User j

Channel Matrix

` ````
```DeepMIMO_dataset{i}.user{j}.channel

Type and Dimensions:

(a) Frequency-domain channel matrix (activate_FD_channels=1)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of OFDM subcarriers)

(b) Time-domain channel matrix (activate_FD_channels=0)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of channel paths)

__Description__: The channel matrix between basestation i and user j. Each of the first two dimensions follows a certain reshaping sequence that can be obtained using the following function:

` ````
```antennamap = antenna_channel_map(params.num_ant_x, params.num_ant_y, params.num_ant_z, 1);

Returns a vector antennamap; each entry in the vector has 3 integers in the form of ‘x y z’ representing the antenna index of the channel element in the x, y, z directions

Note: The DeepMIMO generator **does not** apply an array based normalization for the power. The details on the channel generation can be found in the channel generation document at the bottom of this page.

Line-of-Sight Status

` ````
```DeepMIMO_dataset{i}.user{j}.LoS_status

__Type and Dimensions__: Integer of values {-1, 0, 1}

__Description__: The variable that indicates the existence of the LOS path in the channel.

The values correspond to

(1): The LoS path exists.

(0): Only NLoS paths exist. The LoS path is blocked (LoS blockage).

(-1): No paths exist between the transmitter and the receiver (Full blockage).

TX-RX Distance

` ````
```DeepMIMO_dataset{i}.user{j}.distance

Type__ and Dimensions__: Float

__Description__: The Euclidian distance between the RX and TX locations in meters.

Path loss

` ````
```DeepMIMO_dataset{i}.user{j}.pathloss

Type__ and Dimensions__: Float

__Description__: The combined path-loss of the channel between the RX and TX in dB.

User Location

` ````
```DeepMIMO_dataset{i}.user{j}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

Basestation Location

` ````
```DeepMIMO_dataset{i}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

User Rotation

` ````
```DeepMIMO_dataset{i}.user{j}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the UE antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Basestation Rotation

` ````
```DeepMIMO_dataset{i}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Ray-tracing Path Parameters

` ````
```DeepMIMO_dataset{i}.user{j}.path_params

Type and Dimensions: Structure

Description: The parameters of the ray tracing is provided within the struct. Specifically, for each path

- Azimuth and zenith angle-of-arrival (DoA_phi, DoA_theta)
- Azimuth and zenith angle-of-departure (DoD_phi, DoD_theta)
- Time of arrival (ToA)
- Phase (phase)
- Power (power)
- Number of paths (num_paths)

are provided in this struct.

Azimuth Angles of Departure

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of departure from basestation i, for every channel path traveling from basestation i to user j

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.DoD_phi

Zenith Angles of Departure

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.DoD_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of departure from basestation i, for every channel path traveling from basestation i to user j

Azimuth Angles of Arrival

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.DoA_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of arrival at user j, for each channel path traveling from basestation i to user j

Zenith Angles of Arrival

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.DoA_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of arrival at user j, for each channel path traveling from basestation i to user j

Channel Path Power

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.power

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path power of every channel path traveling from basestation i to user j in watts

Channel Path Phase

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.phase

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path phase of every channel path traveling from basestation i to user j

Note: These phase values are the channel phase shift values before accounting for the phase shifts due to the path propagation delays

Time of Arrival

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.ToA

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path traveling from basestation i to user j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

Number of Paths

` ````
```DeepMIMO_dataset{i}.user{j}.path_params.num_paths

Type and Dimensions: Float

__Description__: The number of paths accounted for when constructing the channel between basestation i and user j.

Time of Arrival*

` ````
```DeepMIMO_dataset{i}.user{j}.ToA

*Note: This is the same array available in path_params.ToA. We copied it here to simplify accessing it when activate_FD_channels=0.

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path traveling from basestation i to user j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

This information is available only if activate_FD_channels=0 (time-domain channel impulse response).

Otherwise, it can be found under the path_params structure as explained next.

#### Output Variables of Basestation i – User j at Scene s

Channel Matrix

` ````
```DeepMIMO_dataset{s}{i}.user{j}.channel

Type and Dimensions:

(a) Frequency-domain channel matrix (activate_FD_channels=1)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of OFDM subcarriers)

(b) Time-domain channel matrix (activate_FD_channels=0)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of channel paths)

__Description__: The channel matrix between basestation i and user j. Each of the first two dimensions follows a certain reshaping sequence that can be obtained using the following function:

` ````
```antennamap = antenna_channel_map(params.num_ant_x, params.num_ant_y, params.num_ant_z, 1);

Returns a vector antennamap; each entry in the vector has 3 integers in the form of ‘x y z’ representing the antenna index of the channel element in the x, y, z directions.

Note: The DeepMIMO generator **does not** apply an array based normalization for the power. The details on the channel generation can be found in the channel generation document at the bottom of this page.

Line-of-Sight Status

` ````
```DeepMIMO_dataset{s}{i}.user{j}.LoS_status

__Type and Dimensions__: Integer of values {-1, 0, 1}

__Description__: The variable that indicates the existence of the LOS path in the channel.

The values correspond to

(1): The LoS path exists.

(0): Only NLoS paths exist. The LoS path is blocked (LoS blockage).

(-1): No paths exist between the transmitter and the receiver (Full blockage).

TX-RX Distance

` ````
```DeepMIMO_dataset{s}{i}.user{j}.distance

Type__ and Dimensions__: Float

__Description__: The Euclidian distance between the RX and TX locations in meters.

Path loss

` ````
```DeepMIMO_dataset{s}{i}.user{j}.pathloss

Type__ and Dimensions__: Float

__Description__: The combined path-loss of the channel between the RX and TX in dB.

User Location

` ````
```DeepMIMO_dataset{s}{i}.user{j}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

Basestation Location

` ````
```DeepMIMO_dataset{s}{i}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

User Rotation

` ````
```DeepMIMO_dataset{s}{i}.user{j}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the UE antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Basestation Rotation

` ````
```DeepMIMO_dataset{s}{i}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Ray-tracing Path Parameters

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params

Type and Dimensions: Structure

Description: The parameters of the ray tracing is provided within the struct. Specifically, for each path

- Azimuth and zenith angle-of-arrival (DoA_phi, DoA_theta)
- Azimuth and zenith angle-of-departure (DoD_phi, DoD_theta)
- Time of arrival (ToA)
- Phase (phase)
- Power (power)
- Number of paths (num_paths)

are provided in this struct.

Azimuth Angles of Departure

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.DoD_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of departure from basestation i, for every channel path traveling from basestation i to user j

Zenith Angles of Departure

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.DoD_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of departure from basestation i, for every channel path traveling from basestation i to user j

Azimuth Angles of Arrival

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.DoA_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of arrival at user j, for each channel path traveling from basestation i to user j

Zenith Angles of Arrival

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.DoA_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of arrival at user j, for each channel path traveling from basestation i to user j

Channel Path Power

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.power

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path power of every channel path traveling from basestation i to user j in watts

Channel Path Phase

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.phase

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path phase of every channel path traveling from basestation i to user j

Note: These phase values are the channel phase shift values before accounting for the phase shifts due to the path propagation delays

Time of Arrival

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.ToA

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path traveling from basestation i to user j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

Number of Paths

` ````
```DeepMIMO_dataset{s}{i}.user{j}.path_params.num_paths

Type and Dimensions: Float

__Description__: The number of paths accounted for when constructing the channel between basestation i and user j.

Time of Arrival*

` ````
```DeepMIMO_dataset{s}{i}.user{j}.ToA

* Note: This is the same array available in path_params.ToA. We copied it here to simplify accessing it when activate_FD_channels=0.

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path between basestation i and user j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

This information is available only if activate_FD_channels=0 (time-domain channel impulse response).

Otherwise, it can be found under the path_params structure as explained next.

#### Output Variables of Basestation i – Basestation j

Channel Matrix

` ````
```DeepMIMO_dataset{i}.basestation{j}.channel

Type and Dimensions:

(a) Frequency-domain channel matrix (activate_FD_channels=1)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of OFDM subcarriers)

(b) Time-domain channel matrix (activate_FD_channels=0)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of channel paths)

__Description__: The channel matrix between basestation i and basestation j. Each of the first two dimensions follows a certain reshaping sequence that can be obtained using the following function:

` ````
```antennamap = antenna_channel_map(params.num_ant_x, params.num_ant_y, params.num_ant_z, 1);

Returns a vector antennamap; each entry in the vector has 3 integers in the form of ‘x y z’ representing the antenna index of the channel element in the x, y, z directions

Note: The DeepMIMO generator **does not** apply an array based normalization for the power. The details on the channel generation can be found in the channel generation document at the bottom of this page.

Line-of-Sight Status

` ````
```DeepMIMO_dataset{i}.basestation{j}.LoS_status

__Type and Dimensions__: Integer of values {-1, 0, 1}

__Description__: The variable that indicates the existence of the LOS path in the channel.

The values correspond to

(1): The LoS path exists.

(0): Only NLoS paths exist. The LoS path is blocked (LoS blockage).

(-1): No paths exist between the transmitter and the receiver (Full blockage).

TX-RX Distance

` ````
```DeepMIMO_dataset{i}.basestation{j}.distance

Type__ and Dimensions__: Float

__Description__: The Euclidian distance between the RX and TX locations in meters.

Path loss

` ````
```DeepMIMO_dataset{i}.basestation{j}.pathloss

Type__ and Dimensions__: Float

__Description__: The combined path-loss of the channel between the RX and TX in dBm.

RX Basestation Location

` ````
```DeepMIMO_dataset{i}.basestation{j}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

TX Basestation Location

` ````
```DeepMIMO_dataset{i}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

RX Basestation Rotation

` ````
```DeepMIMO_dataset{i}.basestation{j}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the RX BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

TX Basestation Rotation

` ````
```DeepMIMO_dataset{i}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the TX BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Ray-tracing Path Parameters

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params

Type and Dimensions: Structure

Description: The parameters of the ray tracing is provided within the struct. Specifically, for each path

- Azimuth and zenith angle-of-arrival (DoA_phi, DoA_theta)
- Azimuth and zenith angle-of-departure (DoD_phi, DoD_theta)
- Time of arrival (ToA)
- Phase (phase)
- Power (power)
- Number of paths (num_paths)

are provided in this struct.

Azimuth Angles of Departure

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.DoD_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of departure from basestation i, for every channel path traveling from basestation i to basestation j

Zenith Angles of Departure

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.DoD_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of departure from basestation i, for every channel path traveling from basestation i to basestation j

Azimuth Angles of Arrival

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.DoA_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of arrival at basestation j, for each channel path traveling from basestation i to basestation j

Zenith Angles of Arrival

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.DoA_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of arrival at basestation j, for each channel path traveling from basestation i to basestation j

Channel Path Power

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.power

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path power of every channel path traveling from basestation i to basestation j in watts

Channel Path Phase

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.phase

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path phase of every channel path traveling from basestation i to basestation j

Note: These phase values are the channel phase shift values before accounting for the phase shifts due to the path propagation delays

Time of Arrival

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.ToA

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path traveling from basestation i to basestation j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

Number of Paths

` ````
```DeepMIMO_dataset{i}.basestation{j}.path_params.num_paths

Type and Dimensions: Float

__Description__: The number of paths accounted for when constructing the channel between basestation i and basestation j.

Time of Arrival*

` ````
```DeepMIMO_dataset{i}.basestation{j}.ToA

* Note: This is the same array available in path_params.ToA. We copied it here to simplify accessing it when activate_FD_channels=0.

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path between basestation i and basestation j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

This information is available only if activate_FD_channels=0 (time-domain channel impulse response).

Otherwise, it can be found under the path_params structure as explained next.

#### Output Variables of Basestation i – Basestation j at Scene s

Channel Matrix

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.channel

Type and Dimensions:

(a) Frequency-domain channel matrix (activate_FD_channels=1)

(b) Time-domain channel matrix (activate_FD_channels=0)

Float matrix of size (number of RX antennas) x (number of TX antennas) x (number of channel paths)

__Description__: The channel matrix between basestation i and basestation j. Each of the first two dimensions follows a certain reshaping sequence that can be obtained using the following function:

` ````
```antennamap = antenna_channel_map(params.num_ant_x, params.num_ant_y, params.num_ant_z, 1);

Returns a vector antennamap; each entry in the vector has 3 integers in the form of ‘x y z’ representing the antenna index of the channel element in the x, y, z directions

**does not** apply an array based normalization for the power. The details on the channel generation can be found in the channel generation document at the bottom of this page.

Line-of-Sight Status

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.LoS_status

__Type and Dimensions__: Integer of values {-1, 0, 1}

__Description__: The variable that indicates the existence of the LOS path in the channel.

The values correspond to

(1): The LoS path exists.

(0): Only NLoS paths exist. The LoS path is blocked (LoS blockage).

(-1): No paths exist between the transmitter and the receiver (Full blockage).

TX-RX Distance

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.distance

Type__ and Dimensions__: Float

__Description__: The Euclidian distance between the RX and TX locations in meters.

Path loss

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.pathloss

Type__ and Dimensions__: Float

__Description__: The combined path-loss of the channel between the RX and TX in dB.

RX Basestation Location

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

TX Basestation Location

` ````
```DeepMIMO_dataset{s}{i}.loc

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The Euclidian location of the user in the form of [x, y, z].

RX Basestation Rotation

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the RX BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

TX Basestation Rotation

` ````
```DeepMIMO_dataset{s}{i}.rotation

Type__ and Dimensions__: Float array of 3 dimensions

__Description__: The rotation applied to the TX BS antenna array in the form of [x_rot, y_rot, z_rot], corresponding to the three rotation angles (in degrees) around the local x-y-z axes

Ray-tracing Path Parameters

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params

Type and Dimensions: Structure

- Azimuth and zenith angle-of-arrival (DoA_phi, DoA_theta)
- Azimuth and zenith angle-of-departure (DoD_phi, DoD_theta)
- Time of arrival (ToA)
- Phase (phase)
- Power (power)
- Number of paths (num_paths)

are provided in this struct.

Azimuth Angles of Departure

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.DoD_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of departure from basestation i, for every channel path traveling from basestation i to basestation j

Zenith Angles of Departure

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.DoD_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of departure from basestation i, for every channel path traveling from basestation i to basestation j

Azimuth Angles of Arrival

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.DoA_phi

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The azimuth angles (directions) of arrival at basestation j, for each channel path traveling from basestation i to basestation j

Zenith Angles of Arrival

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.DoA_theta

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The zenith angles (directions) of arrival at basestation j, for each channel path traveling from basestation i to basestation j

Channel Path Power

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.power

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path power of every channel path traveling from basestation i to basestation j in watts

Channel Path Phase

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.phase

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The channel path phase of every channel path traveling from basestation i to basestation j

Time of Arrival

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.ToA

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path traveling from basestation i to basestation j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

Number of Paths

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.path_params.num_paths

Type and Dimensions: Float

__Description__: The number of paths accounted for when constructing the channel between basestation i and basestation j.

Time of Arrival*

` ````
```DeepMIMO_dataset{s}{i}.basestation{j}.ToA

* Note: This is the same array available in path_params.ToA. We copied it here to simplify accessing it when activate_FD_channels=0.

Type and Dimensions: Float array of size (number of channel paths)

__Description__: The time delay of each channel path between basestation i and basestation j. Each slice from the third dimension of the time domain channel matrix corresponds to a value in the ToA array.

Otherwise, it can be found under the path_params structure as explained next.

## Examples

### Example 1: Introduction to the DeepMIMOv2 Generator

#### The functions used in this example

The main script used to illustrate 'Example 1'

The 'parameters' file used in generating 'Example 1' outputs

### Example 2: Visualization of an Antenna Array Orientation

#### The functions used in this example

The main script used to illustrate 'Example 2'

The 'parameters' file used in generating 'Example 2' outputs

This function is used to visualize the antenna array orientation.

Olivier (2022), PLOTCUBE, MATLAB Central File Exchange. Retrieved February 17, 2022.

### Example 3: Time domain samples at discrete time steps

#### The functions used in this example

The main script used to illustrate 'Example 3'

The 'parameters' file used in generating 'Example 3' outputs

## How Are the Channels Generated?

The following report provides a detailed formulation on the DeepMIMO v2 channel generation process

The detailed formulation of the channel generation process in the DeepMIMO v2 dataset