Enhanced Deep Cooperative Q-Learning for Optimized Vehicle-to-Vehicle Communication in 5G/6G Networks | IEEE Conference Publication | IEEE Xplore

Enhanced Deep Cooperative Q-Learning for Optimized Vehicle-to-Vehicle Communication in 5G/6G Networks

Publisher: IEEE

Abstract:

In the era of 5G and forthcoming 6G, effective Vehicle-to-Vehicle (V2V) communication is crucial for many applications like autonomous driving, real-time traffic informat...View more

Abstract:

In the era of 5G and forthcoming 6G, effective Vehicle-to-Vehicle (V2V) communication is crucial for many applications like autonomous driving, real-time traffic information sharing, and others. This work proposes a novel Enhanced Deep Cooperative Q-Learning (DCO-DQN) model to optimize V2V communication considering the volatile nature of wireless channels, device parameters, vehicular mobility, and history of interactions. The model is equipped with an advanced reward function to reflect multiple performance metrics, which is a clear distinction from existing methods. The comprehensive system model, implementation details, and results clearly show superior performance over traditional methods across various metrics and scenarios. A detailed comparison and analysis strengthen the case for adopting our method for future V2V communication in 5G/6G networks.
Date of Conference: 11-13 October 2023
Date Added to IEEE Xplore: 23 January 2024
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Publisher: IEEE
Conference Location: Jeju Island, Korea, Republic of

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