EESM#

class sionna.sys.EESM(load_beta_table_from: str = 'default', sinr_eff_min_db: float = -30.0, sinr_eff_max_db: float = 30.0, precision: Literal['single', 'double'] | None = None, device: str | None = None)[source]#

Bases: sionna.sys.effective_sinr.EffectiveSINR

Computes the effective SINR from input SINR values across multiple subcarriers and streams via the exponential effective SINR mapping (EESM) method.

Let \(\mathrm{SINR}_{u,c,s}>0\) be the SINR experienced by user \(u\) on subcarrier \(c=1,\dots,C\), and stream \(s=1,\dots,S_c\). If per_stream is False, it computes the effective SINR aggregated across all utilized streams and subcarriers for each user \(u\):

\[\mathrm{SINR}^{\mathrm{eff}}_u = -\beta_u \log \left( \frac{1}{CS} \sum_{c=1}^{C} \sum_{s=1}^{S_c} e^{-\frac{\mathrm{SINR}_{u,c,s}}{\beta_u}} \right), \quad \forall\, u\]

where \(\beta>0\) is a parameter depending on the Modulation and Coding Scheme (MCS) of user \(u\).

If per_stream is True, it computes the effective SINR aggregated across subcarriers, for each user \(u\) and associated stream \(s\):

\[\mathrm{SINR}^{\mathrm{eff}}_{u,s} = -\beta_u \log \left( \frac{1}{C} \sum_{c=1}^{C} e^{-\frac{\mathrm{SINR}_{u,c,s}}{\beta_u}} \right), \quad \forall\, u,s.\]

Parameters:

load_beta_table_from (str) – File name from which the tables containing the values of \(\beta\) parameters are loaded. If 'default', uses the built-in table.
sinr_eff_min_db (float) – Minimum effective SINR value [dB]. Useful to avoid numerical errors. Defaults to -30.
sinr_eff_max_db (float) – Maximum effective SINR value [dB]. Useful to avoid numerical errors. Defaults to 30.
precision (Literal['single', 'double'] | None) – Precision used for internal calculations and outputs. If set to None, precision is used.
device (str | None) – Device for computation. If None, device is used.

Inputs:

sinr – […, num_ofdm_symbols, num_subcarriers, num_ut, num_streams_per_ut], torch.float. Post-equalization SINR in linear scale for different OFDM symbols, subcarriers, users and streams. If one entry is zero, the corresponding stream is considered as not utilized.
mcs_index – […, num_ut], torch.int32. Modulation and coding scheme (MCS) index for each user.
mcs_table_index – […, num_ut], torch.int32 (default: 1). MCS table index for each user.
mcs_category – […, num_ut], torch.int32 (default: None). MCS table category for each user.
per_stream – bool (default: False). If True, then the effective SINR is computed on a per-user and per-stream basis and is aggregated across different subcarriers. If False, then the effective SINR is computed on a per-user basis and is aggregated across streams and subcarriers.

Outputs:

sinr_eff – ([…, num_ut, num_streams_per_ut] | […, num_ut]), torch.float. Effective SINR in linear scale for each user and associated stream. If per_stream is True, then sinr_eff has shape [..., num_ut, num_streams_per_ut], and sinr_eff[..., u, s] is the effective SINR for stream s of user u across all subcarriers. If per_stream is False, then sinr_eff has shape [..., num_ut], and sinr_eff[..., u] is the effective SINR for user u across all streams and subcarriers.

Notes

If the input SINR is zero for a specific stream, the stream is considered unused and does not contribute to the effective SINR computation.

Examples

import torch
from sionna.phy import config
from sionna.sys import EESM
from sionna.phy.utils import db_to_lin

batch_size = 10
num_ofdm_symbols = 12
num_subcarriers = 32
num_ut = 15
num_streams_per_ut = 2

# Generate random MCS indices
mcs_index = torch.randint(0, 27, (batch_size, num_ut))

# Instantiate the EESM object
eesm = EESM()

# Generate random SINR values
sinr_db = torch.rand(batch_size, num_ofdm_symbols, num_subcarriers,
                     num_ut, num_streams_per_ut) * 35 - 5
sinr = db_to_lin(sinr_db)

# Compute the effective SINR for each receiver
sinr_eff = eesm(sinr, mcs_index, mcs_table_index=1, per_stream=False)
print(sinr_eff.shape)
# torch.Size([10, 15])

# Compute the per-stream effective SINR for each receiver
sinr_eff_per_stream = eesm(sinr, mcs_index, mcs_table_index=2,
                           per_stream=True)
print(sinr_eff_per_stream.shape)
# torch.Size([10, 15, 2])

Attributes

property beta_table: Dict#: dict (read-only): Maps MCS indices to the corresponding parameters, commonly called \(\beta\), calibrating the Exponential Effective SINR Map (EESM) method. It has the form beta_table['index'][mcs_table_index][mcs].

property beta_tensor: torch.Tensor#: [n_tables, n_mcs] (read-only): Tensor corresponding to self.beta_table.

property beta_table_filenames: List[str]#: str | list of str: Get/set the absolute path name of the JSON file containing the mapping between MCS and EESM beta parameters, stored in beta.

Methods

validate_beta_table() → bool[source]#

Validates the EESM beta parameter dictionary self.beta_table.

Outputs:: is_valid – True if self.beta_table has a valid structure.
Raises:: ValueError – If the structure is invalid.

EESM#

This Page