# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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"""
Noise variance, RSRP and SINR calculators (NumPy translations of MATLAB detPusch.m).
Implements helpers corresponding to the measurement block in detPusch.m:
- noise_variance_db: compute noise variance per DMRS position (dB) from tmp_noiseVar
- rsrp_db: compute RSRP per DMRS position and UE (dB) from h_est and layer2ue
- sinr_db: compute SINR per DMRS position and UE (dB) as rsrp_db - noise_variance_db
"""
import numpy as np
from ran.types import ComplexArrayNP, FloatArrayNP, FloatNP, IntArrayNP
from ran.utils import db
[docs]
def noise_variance_db(
mean_noise_var: FloatArrayNP,
) -> FloatArrayNP:
"""
Compute noise variance per DMRS position (dB) following detPusch.m.
Args:
mean_noise_var: (n_prb, n_pos) linear noise variances
Returns
-------
noise variance in dB, shape (n_pos,)
"""
# Full-slot: scalar mean over all entries, then repeat per position
return db(np.maximum(np.mean(mean_noise_var, axis=0), np.finfo(float).tiny)) + 0.5
[docs]
def rsrp_db(
h_est: ComplexArrayNP,
layer2ue: IntArrayNP,
n_ue: int,
) -> FloatArrayNP:
"""
Compute RSRP per DMRS position and UE (dB) following detPusch.m.
Args:
h_est: (n_f, nl, n_ant, n_pos) complex channel estimates,
with nf: number of subcarriers in frequency (12*n_prb)
layer2ue: (nl,) int mapping from layer index to UE index (0-based)
n_ue: number of UEs
Returns
-------
rsrp in dB, shape (n_pos, n_ue)
"""
nf, nl, n_ant, n_pos = h_est.shape
# Sum |H|^2 across frequency and antennas -> (nl, n_pos)
per_layer_pos = np.abs(h_est) ** 2
per_layer_pos = per_layer_pos.sum(axis=(0, 2)).astype(FloatNP) # (nl, n_pos)
# Aggregate layers -> UEs: (n_ue, n_pos) then transpose to (n_pos, n_ue)
rsrp_lin = np.zeros((n_ue, n_pos), dtype=FloatNP)
if n_ue > 0 and nl > 0:
np.add.at(rsrp_lin, layer2ue, per_layer_pos)
rsrp_lin = rsrp_lin.T
# Normalize by (nf*nAnt)
rsrp_lin /= nf * max(n_ant, 1)
# Always full-slot average (subslot_proc_option assumed 0)
mean_over_pos = rsrp_lin.mean(axis=0, keepdims=True)
rsrp_lin = np.repeat(mean_over_pos, repeats=n_pos, axis=0)
# Convert to dB
return db(np.maximum(rsrp_lin, np.finfo(float).tiny))
[docs]
def sinr_db(
rsrp_db: FloatArrayNP,
noise_var_db: FloatArrayNP,
) -> FloatArrayNP:
"""
Compute SINR per DMRS position and UE (dB) as rsrp_db - noise_var_db.
Args:
rsrp_db: (n_pos, n_ue) RSRP in dB
noise_var_db: (n_pos,) noise variance in dB
Returns
-------
SINR in dB, shape (n_pos, n_ue)
"""
return rsrp_db - noise_var_db
[docs]
def noise_rsrp_sinr_db(
mean_noise_var: FloatArrayNP,
h_est: ComplexArrayNP,
layer2ue: IntArrayNP,
n_ue: int,
) -> tuple[FloatArrayNP, FloatArrayNP, FloatArrayNP]:
"""
Compute noise variance, RSRP, and SINR per DMRS position and UE (all in dB).
This function sequentially calls noise_variance_db, rsrp_db, and sinr_db
to compute all three metrics in one call.
Args:
mean_noise_var: Noise variance estimate per DMRS position, shape (n_prb, n_pos)
h_est: Channel estimates, shape (n_f, nl, n_ant, n_pos)
layer2ue: Maps layers to UEs (0-indexed), shape (nl,)
n_ue: Number of UEs
Returns
-------
tuple containing:
- noise_var_db: Noise variance in dB, shape (n_pos,)
- rsrp_db_val: RSRP in dB, shape (n_pos, n_ue)
- sinr_db_val: SINR in dB, shape (n_pos, n_ue)
"""
# Compute noise variance in dB
noise_var_db = noise_variance_db(mean_noise_var)
# Compute RSRP in dB
rsrp_db_val = rsrp_db(h_est, layer2ue, n_ue)
# Compute SINR in dB
sinr_db_val = sinr_db(rsrp_db_val, noise_var_db)
return noise_var_db, rsrp_db_val, sinr_db_val
__all__ = [
"noise_rsrp_sinr_db",
"noise_variance_db",
"rsrp_db",
"sinr_db",
]
