Multi Objective Resource Scheduling in LTE Networks using Reinforcement Learning Ioan Sorin Comşa a,b,* , Mehmet Aydin a , Sijing Zhang a , Pierre Kuonen b , Jean–Frederic Wagen b a Institute for Research in Applicable Computing, University of Bedfordshire, Luton, LU1 3JU, United Kingdom b Institute of Information and Communication Technologies, University of Applied Sciences of Western Switzerland, Fribourg, CH-1705, Switzerland ABSTRACT The use of the intelligent packet scheduling process is absolutely necessary in order to make the radio resources usage more efficient in recent high-bit-rate demanding radio access technologies such as Long Term Evolution (LTE). Packet scheduling procedure works with various dispatching rules with different behaviors. In the literature, the scheduling disciplines are applied for the entire transmission sessions and the scheduler performance strongly depends on the exploited discipline. The method proposed in this paper aims to discuss how a straightforward schedule can be provided within the transmission time interval (TTI) sub-frame using a mixture of dispatching disciplines per TTI instead of a single rule adopted across the whole transmission. This is to maximize the system throughput while assuring the best user fairness. This requires adopting a policy of how to mix the rules and a refinement procedure to call the best rule each time. Two scheduling policies are proposed for how to mix the rules including use of Q learning algorithm for refining the policies. Simulation results indicate that the proposed methods outperform the existing scheduling techniques by maximizing the system throughput without harming the user fairness performance. Keywords: LTE, TTI, scheduling rules, scheduling policy, system throughput, user fairness, Q learning, reinforcement learning 1. INTRODUCTION The increase of mobile data usage and the growing demands for new applications (e.g., mobile television, web browsing, File Transfer Protocol, video streaming, Voice over Internet Protocol) have motivated 3 rd Generation Partnership Project (3GPP) to work with LTE (3.9 Generation in Mobile Phones (G)) and LTE-Advanced (LTE-A) (4G), the latest standards of cellular communication technologies. Although the previous technologies, such as Global System for Mobile Communications (GSM)/Enhanced Data rates for GSM Evolution (EDGE) (2G/2.5G) and Universal Mobile Telecommunications System/High Speed Downlink/Uplink Packet Access (UMTS/HSxPA) (3G/3.5G), account at present for over 85% of all mobile subscribers, LTE will provide enhanced performance in comparison with the other mentioned ones. Evolved Universal Terrestrial Radio Access Network (E-UTRAN), the LTE radio access network, offers important benefits for users and operators [1]: performance and capacity, carrier bandwidths flexibility, self-configuration and self-optimization, improved cell capacity, reduced latency and high mobility. These advantages would not have been possible without some aggressive performance requirements for Physical Layer (PHY) and