  In this paper, the concept of Machine Learning (ML) is introduced to the
Orthogonal Frequency Division Multiple Access-based (OFDMA-based) scheduler.
Similar to the impact of the Channel Quality Indicator (CQI) on the scheduler
in the Long Term Evolution (LTE), ML is utilized to provide the scheduler with
pertinent information about the User Equipment (UE) traffic patterns, demands,
Quality of Service (QoS) requirements, instantaneous user throughput and other
network conditions. An adaptive ML-based framework is proposed in order to
optimize the LTE scheduler operation. The proposed technique targets multiple
objective scheduling strategies. The weights of the different objectives are
adjusted to optimize the resources allocation per transmission based on the UEs
demand pattern. In addition, it overcomes the trade-off problem of the
traditional scheduling methods. The technique can be used as a generic
framework with any scheduling strategy. In this paper, Genetic Algorithm-based
(GA-based) multi- objective scheduler is considered to illustrate the
efficiency of the proposed adaptive scheduling solution. Results show that
using the combination of clustering and classification algorithms along with
the GA optimizes the GA scheduler functionality and makes use of the ML process
to form a closed loop scheduling mechanism.
