2. Build Instructions

System Requirements

We tested this system on Ubuntu 22.04 system and it may support other version of Ubuntu. To build this project and make it run properly, the following libraries are needed.

UHD libraries:

sudo add-apt-repository ppa:ettusresearch/uhd
sudo apt-get update
sudo apt-get install libuhd-dev uhd-host

srsRAN_4G's requirements:

sudo apt-get install build-essential cmake libfftw3-dev libmbedtls-dev libboost-program-options-dev libconfig++-dev libsctp-dev

yaml-cpp:

# In a different directory
git clone https://github.com/jbeder/yaml-cpp.git
cd yaml-cpp
mkdir build
cd build
cmake ..
make
sudo make install

We need liquid-dsp for resampling if better-fidelity TwinRX USRP X310 daughterboard is used:

# In a different directory
sudo apt-get install automake autoconf
# download source codes
git clone https://github.com/jgaeddert/liquid-dsp.git
cd liquid-dsp
# Building and installing the main library
./bootstrap.sh
./configure
make
sudo make install
sudo ldconfig
# to double check, libs should appear at /usr/local/lib and header liquid.h should appear at /usr/local/include/liquid/

For different USRP daughterboard, different config.yaml should be used. Please refer to the sample config.yaml in ./nrscope/config/config.yaml and the explanatory comment in it.

CBX:

......
rf_args: "clock=external,type=x300,sampling_rate=23040000" #"type=x300" #"clock=external"
rx_gain: 30 # for x310, max rx gain is 31.5, for b210, it's around 80
srate_hz: 23040000 #11520000 #11520000 #23040000
srsran_srate_hz: 23040000
......

TwinRX (note TwinRX has a significantly higher rx gain limit):

......
rf_args: "clock=external,type=x300,master_clock_rate=200000000,sampling_rate=25000000" #"type=x300" #"clock=external"
rx_gain: 90 # for x310, max rx gain is 31.5, for b210, it's around 80
srate_hz: 25000000 #11520000 #11520000 #23040000
srsran_srate_hz: 23040000
......

Also, you should turn on the performance mode:

# This script is copied from srsRAN_Project
cd script
sudo ./srsran_performance

Push DCI logs to google storage BigQuery table (optional): Using google client c++ library requires c++ 14.0, where in ubuntu 22.04, the default c++ version is 11.0. We thought that nobody wants to mess with the system compiling environments, so we implement the function of pushing data to google cloud storage with python. Here is a step-by-step instruction of how to push the DCI log to our google cloud storage:

1. sudo pip install google-cloud-storage geocoder
2. sudo gcloud auth application-default login --impersonate-service-account [email protected]
3. # There will be a link from goole, open that link and login with any of your google account.
4. sudo gcloud init
5. # In the config file (./nrscope/config.yaml), set push_to_google: true.
6. # In the config file (./nrscope/config.yaml), set google_service_account_credential: "/home/wanhr/Downloads/nsf-2223556-222187-b5d2ea50f5d1.json" with the google service account credential file (provided by us)'s location on your file system.
7. # In the config file (./nrscope/config.yaml), set google_dataset_id: "ngscope5g_dci_log" with the google cloud Bigquery dataset name, everyone has his/her own dataset. The code will create one if the dataset with this dataset id is not existed.
8. sudo gcloud auth application-default set-quota-project <Your Google Storage Project Name>

Files and functions

Entry: /nrscope/src/main.cc
Load config: /nrscope/src/libs/load_config.cc
Radio thread (cell search, mib decoding, coreset decoding, etc.): /nrscope/src/libs/radio_nr.cc
Config file: /nrscope/config/config.yaml
Cell scan entry (scan all GSCN/SSB points): /nrscope/src/scan_main.cc

Usage

mkdir build
cd build
cmake ../
make all -j ${nof_proc}
cd nrscope/src/
sudo ./nrscope | tee ./$(date +"%Y-%m-%d_%H:%M:%S").txt | grep Found # This command can save the trace and show if cell or DCIs are found.

# or to scan all 5G SA cells (in nrscope/src/)
sudo ./nrscan