TITLE :- ENHANCING CELLULAR VEHICLE-TO-EVERYTHING (C-V2X) COMMUNICATION THROUGH ADAPTIVE MCS INDEX CONTROL WITH REINFORCEMENT LEARNING
------------------------------------------------------------------------------------------------------------------------

Implementation Plan:
---------------------------

Tools:

Software Tools -- Omnet 4.6, SUMO-0.19.0

Operating System -- Windows 10 [64-bit]

Project workflow:

Ø We Construct a network with 100- Vehicles, 2- Rsu, and 1 - Base station.

Ø The proposed work includes Four processes which are listed as follows,
sumo configuration
Communication Model
Q-learning Algorithm


Ø sumo configuration : The placement of RSU and Vehicles depends on the sumo configuration.

Ø Communication Model : Next, We Implement communication model using C-V2X standards.

Ø Q-learning Algorithm: Next, We Integrate the reinforcement learning (RL) algorithm using the Q-learning approach

Ø Finally, The performance of this research is evaluated by the following metrics,

Throughput
Latency
Packet Loss Rate
Energy Efficiency

Note:
--------

1. If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2. Please note that this implementation plan does not include any further steps after it is put into implementation.
------------------------------------------------------------------------------------------------------------------
#V2XPerformance
#ConnectedVehicles
#V2XCommunication
#SmartTransportation
#VehicleTechnology
#DataAnalysis
#TrafficSafety
#IoTInnovation
#FutureMobility
#wirelesscommunication
-------------------------------------------------------------------------------------------------------------------
All types of network simulation tools support for your network research ideas such as
NS3, OMNET++, MATLAB, MININET, NS2, OPNET, PYTHON, COOJA, CONTIKI OS, QUALNET,
OPNET, NETSIM, GNS3, RTOOL, OPTISYSTEM, ONESIM …. will be supported under one roof.

We would be delighted to extend our support in utilizing network simulation tools to effectively resolve any network research challenges that you may encounter.

For further support

visit us at : https://networksimulationtools.com

Mail us at : [email protected]

call us at : 94448 47435