PhD in Reinforcement V2V Communication in 5G Network

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PhD in Reinforcement Learning V2V Communication Using 5G Network


Vehicle-to-Everything or V2X communication has become the focus because of its capability to schedule multiple access in an effective manner. This includes the Dedicated Short Term Range Communication model, also referred to as DSRC. It comprises of a unique feature in which each of the vehicles undergoes an ever changing environment that varies as per mobility. Despite its robust structure and functions there is a need of a reinforcement  based Vehicle to vehicle communication using 5G Network.

Research Approach

The research proposal for reinforcement learning based V2V communication using 5G Network is based on the idea according to which a vehicle autonomously enriches knowledge on the surroundings over time and notifies the V2X networking parameters.

The reinforcement learning based V2X communication using 5G Network architecture focuses on decreasing the load of a C-V2X network. The proposed technique is compatible to function at each vehicle without any support from the central entity. This framework is expected to structure a network that can fit to the distributed V2X. This is self-implied where the nodes are directly connected without going through the network core.


Because of high mobility and dynamicity, it is a challenge to lighten the load of communication in the vehicle for decreasing latency and increasing reliability. Though multiple models have been proposed in the past as reinforcement learning based V2X communication using 5G Network solutions, but due to the complexity of the parent structure, those couldn’t be incorporated effectively.

PhD in Reinforcement Learning V2X Communication Using 5G Network

Proposed Solution

The 3GPP initiated sidelinking to take a vital technical basis in handling both basic and advanced safety cases. The Physical sidelink feedback channel carries a Hybrid automatic repeat request from a recipient vehicle of a message which is in either of the two forms. Then to reduce the input dimension types of drivers’ dangerous behavior are enlisted as it is the key factor for crashing.

The problem formulated can be denoted as:

As per the proposed reinforcement learning based V2V communication using 5G Network, a variant of the 0-1 knapsack problem offers to increase the reward while maintaining the cost under a certain threshold level.

The equation for determining the optimal  is written as follows:

Future Proposal

The future proposal can be regarding the “relaxation” of the regression of the behavior of the vehicle’s driver. This will be based on the proposed reinforcement learning based V2V communication using 5G Network.

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