Deep Reinforcement Learning Based Dynamic Resource Allocation in 5G Networks
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https://doi.org/10.5281/zenodo.8416084Keywords:
Deep learning (DL), base station (BS), deep Q-learning (DQL) algorithmAbstract
The rapid proliferation of 5G technologies necessitates the development of new strategies for managing network resources. Resources are traditionally allocated using heuristic approaches like exhaustive search and genetic algorithms, as well as combinatorial techniques such as branch and bound. Large-scale heterogeneous cellular networks with ultra-dense base station installations, huge connections, and varying QoS needs for distinct classes of users can not benefit from these solutions due to their computational cost. There is a need for a paradigm change from traditional resource allocation algorithms in the fifth generation of wireless networks as a result. Methods for optimizing performance with a low computing cost have been created using data-driven Machine Learning (ML). Deep learning (DL) is a useful technique for training a multi-layer neural network to simulate a resource management method from network data. Thereby avoiding heavy online computations that would otherwise be necessary to solve resource allocation problems might be achieved. For multi-cell wireless networks, we create a deep learning-based resource allocation framework with the goal of increasing total network throughput in this research. We train and test multiple reinforcement learning agents using the deep Q-networks (DQN) algorithm, and the so-called Rain-bow extensions of DQN. The performance of each agent is tested on 5G Urban Macro simulation scenarios, and is benchmarked against a fixed power allocation approach.
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