Abstract
In this paper a new optimization technique has been proposed to take the advantage of both the Hungarian Algorithm and Deep Q-Learning Neural Network (DQN) to solve the frequency and power resources allocation problem in Vehicle to Vehicle (V2V) future networks. The result shows a better performance for the sum of cellular users throughput, reducing the complexity of the classical optimization methods, overcome the huge State-Action matrix in Q-learning and provides wireless environment features to approximate the Q-values.
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Hamdan, M., Hamdi, K. (2019). Combining Machine Learning and Classical Optimization Techniques in Vehicle to Vehicle Communication Network. In: Yin, H., Camacho, D., Tino, P., Tallón-Ballesteros, A., Menezes, R., Allmendinger, R. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2019. IDEAL 2019. Lecture Notes in Computer Science(), vol 11871. Springer, Cham. https://doi.org/10.1007/978-3-030-33607-3_38
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DOI: https://doi.org/10.1007/978-3-030-33607-3_38
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