Multi objective resource scheduling in LTE networks using reinforcement learning

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.