Optimizing Performance of Worst Case User in Ultra-dense Networks Utilizing Deep Q-learning

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Abstract: This paper defines, analyzes, and improves the performance of the worst-case user in ultradense networks Background: In Ultra-Dense Networks (UDNs), where the Base Stations are distributed with a very high density, the users are possibly near the cells’ intersection. These users are called the Worst-Case Users (WCU) and usually experience very low performance. Objectives: Thus, improving the WCU performance is an urgent problem to secure the service requirement of future cellular networks. Methods: In this paper, the performance of the WCU is analyzed in UDNs with a maximum power algorithm and under the wireless environment with Stretched Path Loss model and Rayleigh fading. To improve the WCU data rate, the Deep Q Networks with and without Multi-Input-Multi-output (MIMO) are utilized in this paper. Results: The simulation results show that a system–based Deep Q Learning can dramatically improve the WCU performance compared to the system with the maximum power algorithm. In addition, the deployment of the MIMO technique in a system–based Deep Q-learning only has benefits in bad channel conditions. Conclusion: In any channel condition, utilization of Deep Q Learning is a suitable solution to improve the WCU performance. Furthermore, if the user experiences a good channel condition, the MIMO technique can be used with Deep Q Learning to obtain further performance improvement.