Enhancing 5G Network Throughput Using Reinforcement Learning

In this research, RL is proposed as a solution towards achieving the dynamic optimization of 5G network throughput hindered by the complexity of Intersystems environments. To this extent, the optimization problem is formulated and modeled within the framework of Markov Decision Process and the study resorts to techniques from Deep Reinforcement Learning leading to the design of adaptive algorithms that optimise various network parameters in real-time. It uses simulated raw 5G network data and other data open for public usage for training it and the application itself is coded in Python programming language with TensorFlow for implementing different algorithms. As for the performance, it could be seen that the RL-optimized approach is superior to traditional methods and leads to better system performance in terms of throughput, latency, energy efficiency and user satisfaction in a number of scenarios. The study identifies the possibility to apply RL to enable the learning capability of 5G networks in supporting new services and optimizing the network performance. This research finding can be beneficial for the advancement of 5G wireless communication technology by demonstrating the versatility and advantages of utilizing RL in the network.