  Resource allocation is still a difficult issue to deal with in wireless
networks. The unstable channel condition and traffic demand for Quality of
Service (QoS) raise some barriers that interfere with the process. It is
significant that an optimal policy takes into account some resources available
to each traffic class while considering the spectral efficiency and other
related channel issues. Reinforcement learning is a dynamic and effective
method to support the accomplishment of resource allocation properly
maintaining QoS levels for applications. The technique can track the system
state as feedback to enhance the performance of a given task. Herein, it is
proposed a simple reinforcement learning mechanism introduced in LTE-A networks
and aimed to choose and limit the number of resources allocated for each
traffic class, regarding the QoS Class Identifier (QCI), at each Transmission
Time Interval (TTI) along the scheduling procedure. The proposed mechanism
implements a Markov Decision Process (MDP) solved by the Q-Learning algorithm
to find an optimal action-state decision policy. The results obtained from
simulation exhibit good performance, especially for the real-time Video
application.
