# 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
#
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"""
NumPy implementation of MATLAB-style PUSCH DMRS generation utilities.
This module mirrors the logic used in the MATLAB pipeline for building
frequency-domain DMRS sequences and their underlying Gold sequences.
It is designed to be compatible with the channel estimation code that
consumes these sequences (see `_apply_chest_ls_main.py`).
Key functions:
- gen_dmrs_sym: produce per-symbol, per-SCID DMRS resources
"""
import numpy as np
from ran.phy.numpy.utils import gold_sequence, qpsk_map
from ran.types import ComplexArrayNP, ComplexNP, IntArrayNP, IntNP
from ran.constants import N_SYM_PER_SLOT
[docs]
def gen_dmrs_sym(
slot_number: int,
n_f: int,
n_dmrs_id: int,
sym_idx_dmrs: IntArrayNP | None = None,
*,
n_t: int = 14,
) -> tuple[ComplexArrayNP, IntArrayNP]:
"""Generate DMRS symbols and scrambling sequences.
Parameters
----------
slot_number : int
Integer slot number
n_f : int
Length of Gold sequence per port (must be even)
n_dmrs_id : int
DMRS identity (integer)
sym_idx_dmrs : IntArrayNP | None, optional
0-based indices of DMRS symbols to generate.
Alternatively, provide n_t (number of OFDM symbols) to generate indices [0, 1, ..., n_t-1].
Exactly one of sym_idx_dmrs or n_t must be provided.
n_t : int, default=14
Number of OFDM symbols
Returns
-------
r_dmrs : ComplexArrayNP
Complex array of shape (n_f//2, n_sym, 2), with 2 = number of SCIDs
scr_seq : IntArrayNP
Integer array of shape (n_f, n_sym, 2)
"""
if (n_f % 2) != 0:
msg = "n_f must be even to form complex DMRS from Gold sequence"
raise ValueError(msg)
if sym_idx_dmrs is None:
sym_idx_dmrs = np.arange(n_t, dtype=IntNP)
t_idx_vec: IntArrayNP = sym_idx_dmrs.astype(IntNP) + 1
n_sym = t_idx_vec.size
r_dmrs: ComplexArrayNP = np.empty((n_f // 2, n_sym, 2), dtype=ComplexNP)
scr_seq: IntArrayNP = np.empty((n_f, n_sym, 2), dtype=IntNP)
# n_scid_vec: 1D array containing the two possible scrambling identities (SCID)
n_scid_vec = np.array([0, 1], dtype=IntNP)
# Compute c_init for all (t_idx, n_scid) pairs: shape (n_t, 2)
c_init_mat = (
(1 << 17) * (slot_number * N_SYM_PER_SLOT + t_idx_vec[:, None]) * (2 * n_dmrs_id + 1)
+ 2 * n_dmrs_id
+ n_scid_vec[None, :]
) % (1 << 31)
# For each (t_idx, n_scid), generate Gold sequence and QPSK map
for scid_idx in range(2): # [0, 1]
for t_idx in range(n_sym):
c_init = c_init_mat[t_idx, scid_idx]
c = gold_sequence(c_init, n_f)
scr_seq[:, t_idx, scid_idx] = c
r_dmrs[:, t_idx, scid_idx] = qpsk_map(c, n_f // 2)
return r_dmrs, scr_seq
