umi.py (5201B)
1 # 2 # SPDX-FileCopyrightText: Copyright (c) 2021-2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. 3 # SPDX-License-Identifier: Apache-2.0 4 # 5 """Urban microcell (UMi) channel model from 3GPP TR38.901 specification""" 6 7 import tensorflow as tf 8 9 from . import SystemLevelChannel 10 from . import UMiScenario 11 12 13 class UMi(SystemLevelChannel): 14 # pylint: disable=line-too-long 15 r"""UMi(carrier_frequency, o2i_model, ut_array, bs_array, direction, enable_pathloss=True, enable_shadow_fading=True, always_generate_lsp=False, dtype=tf.complex64) 16 17 Urban microcell (UMi) channel model from 3GPP [TR38901]_ specification. 18 19 Setting up a UMi model requires configuring the network topology, i.e., the 20 UTs and BSs locations, UTs velocities, etc. This is achieved using the 21 :meth:`~sionna.channel.tr38901.UMi.set_topology` method. Setting a different 22 topology for each batch example is possible. The batch size used when setting up the network topology 23 is used for the link simulations. 24 25 The following code snippet shows how to setup a UMi channel model operating 26 in the frequency domain: 27 28 >>> # UT and BS panel arrays 29 >>> bs_array = PanelArray(num_rows_per_panel = 4, 30 ... num_cols_per_panel = 4, 31 ... polarization = 'dual', 32 ... polarization_type = 'cross', 33 ... antenna_pattern = '38.901', 34 ... carrier_frequency = 3.5e9) 35 >>> ut_array = PanelArray(num_rows_per_panel = 1, 36 ... num_cols_per_panel = 1, 37 ... polarization = 'single', 38 ... polarization_type = 'V', 39 ... antenna_pattern = 'omni', 40 ... carrier_frequency = 3.5e9) 41 >>> # Instantiating UMi channel model 42 >>> channel_model = UMi(carrier_frequency = 3.5e9, 43 ... o2i_model = 'low', 44 ... ut_array = ut_array, 45 ... bs_array = bs_array, 46 ... direction = 'uplink') 47 >>> # Setting up network topology 48 >>> # ut_loc: UTs locations 49 >>> # bs_loc: BSs locations 50 >>> # ut_orientations: UTs array orientations 51 >>> # bs_orientations: BSs array orientations 52 >>> # in_state: Indoor/outdoor states of UTs 53 >>> channel_model.set_topology(ut_loc, 54 ... bs_loc, 55 ... ut_orientations, 56 ... bs_orientations, 57 ... ut_velocities, 58 ... in_state) 59 >>> # Instanting the frequency domain channel 60 >>> channel = OFDMChannel(channel_model = channel_model, 61 ... resource_grid = rg) 62 63 where ``rg`` is an instance of :class:`~sionna.ofdm.ResourceGrid`. 64 65 Parameters 66 ----------- 67 68 carrier_frequency : float 69 Carrier frequency in Hertz 70 71 o2i_model : str 72 Outdoor-to-indoor loss model for UTs located indoor. 73 Set this parameter to "low" to use the low-loss model, or to "high" 74 to use the high-loss model. 75 See section 7.4.3 of [TR38901]_ for details. 76 77 rx_array : PanelArray 78 Panel array used by the receivers. All receivers share the same 79 antenna array configuration. 80 81 tx_array : PanelArray 82 Panel array used by the transmitters. All transmitters share the 83 same antenna array configuration. 84 85 direction : str 86 Link direction. Either "uplink" or "downlink". 87 88 enable_pathloss : bool 89 If `True`, apply pathloss. Otherwise doesn't. Defaults to `True`. 90 91 enable_shadow_fading : bool 92 If `True`, apply shadow fading. Otherwise doesn't. 93 Defaults to `True`. 94 95 always_generate_lsp : bool 96 If `True`, new large scale parameters (LSPs) are generated for every 97 new generation of channel impulse responses. Otherwise, always reuse 98 the same LSPs, except if the topology is changed. Defaults to 99 `False`. 100 101 dtype : Complex tf.DType 102 Defines the datatype for internal calculations and the output 103 dtype. Defaults to `tf.complex64`. 104 105 Input 106 ----- 107 108 num_time_steps : int 109 Number of time steps 110 111 sampling_frequency : float 112 Sampling frequency [Hz] 113 114 Output 115 ------- 116 a : [batch size, num_rx, num_rx_ant, num_tx, num_tx_ant, num_paths, num_time_steps], tf.complex 117 Path coefficients 118 119 tau : [batch size, num_rx, num_tx, num_paths], tf.float 120 Path delays [s] 121 """ 122 123 def __init__(self, carrier_frequency, o2i_model, ut_array, bs_array, 124 direction, enable_pathloss=True, enable_shadow_fading=True, 125 always_generate_lsp=False, dtype=tf.complex64): 126 127 # RMa scenario 128 scenario = UMiScenario(carrier_frequency, o2i_model, ut_array, bs_array, 129 direction, enable_pathloss, enable_shadow_fading, 130 dtype) 131 132 super().__init__(scenario, always_generate_lsp)