Kavli Affiliate: Zeeshan Ahmed
| First 5 Authors: Riya Dinesh Deshpande, Faheem A. Khan, Qasim Zeeshan Ahmed, ,
| Summary:
As the number of devices getting connected to the vehicular network grows
exponentially, addressing the numerous challenges of effectively allocating
spectrum in dynamic vehicular environment becomes increasingly difficult.
Traditional methods may not suffice to tackle this issue. In vehicular networks
safety critical messages are involved and it is important to implement an
efficient spectrum allocation paradigm for hassle free communication as well as
manage the congestion in the network. To tackle this, a Deep Q Network (DQN)
model is proposed as a solution, leveraging its ability to learn optimal
strategies over time and make decisions. The paper presents a few results and
analyses, demonstrating the efficacy of the DQN model in enhancing spectrum
sharing efficiency. Deep Reinforcement Learning methods for sharing spectrum in
vehicular networks have shown promising outcomes, demonstrating the system’s
ability to adjust to dynamic communication environments. Both SARL and MARL
models have exhibited successful rates of V2V communication, with the
cumulative reward of the RL model reaching its maximum as training progresses.
| Search Query: ArXiv Query: search_query=au:”Zeeshan Ahmed”&id_list=&start=0&max_results=3