5g-channel 中文文档教程

发布于 4年前 浏览 25 项目主页 更新于 3年前

5g_channel

5G NR 信道模型:目前仅实现了路径损耗 (7.4.1) 和 LOS 概率 (7.4.2)

Reference

3GPP TR 38.901 V16:研究 0.5 至 100 GHz 频率的信道模型,第 7 章 0.5-100 的信道模型GHz

Usage

选项 1:导入所有函数

const g5_channel = require('5g-channel');

g5_channel.pathloss.pathloss_rma_los(2, 400); // 2 GHz,400 米距离

g5_channel.prLos.pr_los_rma(400);

选项 2:选择性导入

const { pathloss_rma_los } = require('5g-channel/pathloss') ;

pathloss_rma_los(2, 400)

Includes: pathloss and line-of-sight probability for the following scenarios

## Rural Macro

  • LOS概率:pr_los_rma(d_2d_out)

  • Pathloss:

    • LOS: pathloss_rma_los(fc, d_2D, h_BS, h_UT, W, h)
    • NLOS: pathloss_rma_nlos(fc, d_2D, h_BS, h_UT, W, h)
    • Average: pathloss_rma(fc, d_2D, h_BS, h_UT, W, h)

    Urban Macro

  • LOS概率:pr_los_uma(d_2d_out, h_UT)

  • > 路径损失:

    • LOS: pathloss_uma_los(fc, d_2D, h_BS, h_UT, h_E)
    • NLOS: pathloss_uma_nlos(fc, d_2D, h_BS, h_UT, h_E, option)
    • Average: pathloss_uma(fc, d_2D, h_BS, h_UT, h_E, option)

    Urban Micro-streen canyon

  • LOS概率:pr_los_umi(d_2d_out) 路径损失

    • LOS: pathloss_umi_los(fc, d_2D, h_BS, h_UT)
    • NLOS: pathloss_umi_nlos(fc, d_2D, h_BS, h_UT, option)
    • Average: pathloss_umi(fc, d_2D, h_BS, h_UT, option)

    Indoor-office (InH)

  • LOS概率:

    • mixed office: pr_los_inh_mixed(d_2d_in)
    • open office: LOS probability: pr_los_inh_open(d_2d_in)
    • both types: LOS probability: pr_los_inh(type, d_2d_in)
  • Pathloss:

    • LOS: pathloss_inh_los(fc, d_3D)
    • NLOS: pathloss_inh_nlos(fc, d_3D, option)
    • Average: pathloss_inh(fc, d_3D, d_2D, type, option)

    Infoor Factory (InF)

  • LOS概率:pr_los_inf(type, d_2d, h_BS, h_UT, h_c, r)

  • 路径损失:

    • LOS: pathloss_inf_los(fc, d_3D)
    • NLOS: pathloss_inf_nlos(fc, d_3D, type)
    • Average: pathloss_inf(fc, d_3D, h_BS, h_UT, h_c, r, type)

    Different types of InF

    • InF-SL (sparse clutter, low BS)
    • InF-DL (dense clutter, low BS)
    • InF-SH (sparse clutter, high BS)
    • InF-DH (dense clutter, high BS)
    • InF-HH (high Tx, high Rx)

    A top-level pathloss function for all scenarios

    路径损失(场景、los、fc、h_BS、h_UT、W、h、d_2D、类型、选项、h_c、r)

5g_channel

5G NR channel model: currently only implemented pathloss (7.4.1) and LOS probability (7.4.2)

Reference

3GPP TR 38.901 V16: Study on channel model for frequencies from 0.5 to 100 GHz, Chapter 7 Channel model(s) for 0.5-100 GHz

Usage

Option 1: import all functions

const g5_channel = require('5g-channel');

g5_channel.pathloss.pathloss_rma_los(2, 400); // 2 GHz, 400m distance

g5_channel.prLos.pr_los_rma(400);

Option 2: selective import

const { pathloss_rma_los } = require('5g-channel/pathloss');

pathloss_rma_los(2, 400)

Includes: pathloss and line-of-sight probability for the following scenarios

## Rural Macro

  • LOS probability: pr_los_rma(d_2d_out)

  • Pathloss:

    • LOS: pathloss_rma_los(fc, d_2D, h_BS, h_UT, W, h)
    • NLOS: pathloss_rma_nlos(fc, d_2D, h_BS, h_UT, W, h)
    • Average: pathloss_rma(fc, d_2D, h_BS, h_UT, W, h)

    Urban Macro

  • LOS probability: pr_los_uma(d_2d_out, h_UT)

  • Pathloss:

    • LOS: pathloss_uma_los(fc, d_2D, h_BS, h_UT, h_E)
    • NLOS: pathloss_uma_nlos(fc, d_2D, h_BS, h_UT, h_E, option)
    • Average: pathloss_uma(fc, d_2D, h_BS, h_UT, h_E, option)

    Urban Micro-streen canyon

  • LOS probability: pr_los_umi(d_2d_out)

  • Pathloss:

    • LOS: pathloss_umi_los(fc, d_2D, h_BS, h_UT)
    • NLOS: pathloss_umi_nlos(fc, d_2D, h_BS, h_UT, option)
    • Average: pathloss_umi(fc, d_2D, h_BS, h_UT, option)

    Indoor-office (InH)

  • LOS probability:

    • mixed office: pr_los_inh_mixed(d_2d_in)
    • open office: LOS probability: pr_los_inh_open(d_2d_in)
    • both types: LOS probability: pr_los_inh(type, d_2d_in)
  • Pathloss:

    • LOS: pathloss_inh_los(fc, d_3D)
    • NLOS: pathloss_inh_nlos(fc, d_3D, option)
    • Average: pathloss_inh(fc, d_3D, d_2D, type, option)

    Infoor Factory (InF)

  • LOS probability: pr_los_inf(type, d_2d, h_BS, h_UT, h_c, r)

  • Pathloss:

    • LOS: pathloss_inf_los(fc, d_3D)
    • NLOS: pathloss_inf_nlos(fc, d_3D, type)
    • Average: pathloss_inf(fc, d_3D, h_BS, h_UT, h_c, r, type)

    Different types of InF

    • InF-SL (sparse clutter, low BS)
    • InF-DL (dense clutter, low BS)
    • InF-SH (sparse clutter, high BS)
    • InF-DH (dense clutter, high BS)
    • InF-HH (high Tx, high Rx)

    A top-level pathloss function for all scenarios

    pathloss(scenario, los, fc, h_BS, h_UT, W, h, d_2D, type, option, h_c, r)

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