All resource list for all resource of this project

• slides, docs, etc, in shared google drive: https://drive.google.com/drive/folders/1RxiAF65TxsQQYNlToQf0eLN43lpVKUC6?usp=sharing
• slack communication team: https://5gopt.slack.com/

telecommunication

• python manage.py runserver

file description in this project

| instructions_For_install_MongoCXXDriver.txt | how to install mongo cxx driver to implement communication between ns3 controller and mongoDB | | media/ | folder for domination map which is dynamically generated in backend and updates when new documents add in mongo | installShell.sh | script for automatically set up the development environment(not finish) |

Create a new mongo user

• db.createUser({user:"", pwd:"****", roles:[{role:"userAdminAnyDatabase", db:"admin"}]})

create a new database

• use newDatabase

import data into mongo database

• mongoimport --db 5gopt --collection normal --type csv --file ~/code/data/normal_800m.csv --headerline --ignoreBlanks
• mongoimport --db 5gopt --collection main_file_with_UserTHR --type csv --file ~/Downloads/main_file_with_UserTHR.csv --fields "Time,LocationX,LocationY,UserID,CellID,RSRP,RSRQ,SINR,UserThR" --ignoreBlanks
• mongoexport --db 5gopt --collection normal --out traffic.json

install mongo c driver and mongo cxx driver to implment communication between ns-allinone-3.26 and mongo v2.6.12

• cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_INSTALL_PREFIX=/usr/local -DCMAKE_PREFIX_PATH=/usr/local ..

restore bson file into mongodb

• mongorestore -d db_name -c collection_name path/file.bson
• ./mongorestore -d 5gopt -c normal /home/tupevarj/Downloads/normal.bson
• ./mongorestore -d 5gopt -c controlpanel /home/tupevarj/Downloads/controlpanel.bson
• ./mongorestore -d 5gopt -c outage /home/tupevarj/Downloads/outage.bson
• ./mongorestore -d 5gopt -c event_log /home/tupevarj/Downloads/event_log.bson
• ./mongorestore -d 5gopt -c main_file_with_UserTHR /home/tupevarj/Downloads/main_file_with_UserTHR.bson

development environment necessary libraries

| libraries | version | command to install | | python | 3.6.3 | anaconda 3 | | mongo | 2.6.12 | ......... | | django | 1.11.7 | [pip install Django==1.11.7] | | MongoDB shell version | 2.6.12 | ..... | | pymongo | 3.5.1 | [pip install pymongo==3.1.1] | | OS | RHEL 7 | ...... | | pycharm | 2017.2.4 | ... |

| Database name | 5gopt | | DB user name | ... | | DB password | ... |

related documentation for this project

• MongoDB C Driver 1.9.0 [http://mongoc.org/libmongoc/current/index.html]
• MongoDB C++ Driver manual [https://mongodb.github.io/mongo-cxx-driver/]
• PyMongo 3.6.0 Documentation [https://api.mongodb.com/python/current/]
• MongoEngine Documentation [http://docs.mongoengine.org/]

Match between display figures in front end and database

Display in Front End	Data in MongoDB
unnormal cell detection table	Fake data in Backend not from Mongo
Domination Map	dominationmap
throughput line graph	main_file_with_UserTHR

Collection schemas

dominationmap	x	y	z	sinr
Demo	-250	-250	1.5	1.45

main_file_with_UserTHR	Time	UserThR	LocationX	UserID	CellID	RSRP	RSRQ	SINR	LocationY
Demo	0.2	NaN	948	1	1	-113	NaN	NaN	1429

event_log	"TIME"	"X"	"S"	"IMSI"	A2 RSRQ Enter	RSRQ	CellID
Demo	8	964	423	19		-9.315	14

handover_log	"TIME"	"X"	"Y"	"IMSI"	EVENT	"CellID"
Demo	12.26	449	449	20	"Handover End OK"	11

normal(outage)	Time	UeNodeNo	UeRNTI	RSRP	Serving_Cell	RSRQ	Cell_ID
Demo	0,2	21	0	-89,9397	0	-nan	20

controlpanel	cellID	normal	outage	coc	cco	mro	mlb	dirty_flag
Demo	5	0	1	0	0	0	0	0

run ns3 command

• ns-3.26>$ ./waf --run="CSONDemo --simTime=5 --ns3::LteHelper::HandoverAlgorithm=ns3::A3RsrpHandoverAlgorithm"

to do list

1. set up Apache when it's ready to deploy Django in production.
2. preprocessing the data, which the final table schema should be like the following:

Time	User	LocationX	LocationY	RSRP_1st	1stRSRP_Corresponding_RSRQ	RSRP_2nd	2ndRSRP_Corresponding_RSRQ	RSRP_3rd	3rdRSRP_Corresponding_RSRQ	RSRP_4th	4thRSRP_Corresponding_RSRQ	Serving Cell
0.2	1	..	..	..	..
0.2	2	..
0.2	..	..
0.4	1	..	..
0.4	2	..	..
Explaination
Serving Cell: pick the cell number which its rsrp value is the biggest, therefore, pick the rsrp_1st corresponding cell
calculate all users at specific time point, then for the next time point, repeat the above process.
the final data schema should be following: