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In this paper, the concept of Machine Learning (ML) is introduced to theOrthogonal Frequency Division Multiple Access-based (OFDMA-based) scheduler.Similar to the impact of the Channel Quality Indicator (CQI) on the schedulerin the Long Term Evolution (LTE), ML is utilized to provide the scheduler withpertinent information about the User Equipment (UE) traffic patterns, demands,Quality of Service (QoS) requirements, instantaneous user throughput and othernetwork conditions. An adaptive ML-based framework is proposed in order tooptimize the LTE scheduler operation. The proposed technique targets multipleobjective scheduling strategies. The weights of the different objectives areadjusted to optimize the resources allocation per transmission based on the UEsdemand pattern. In addition, it overcomes the trade-off problem of thetraditional scheduling methods. The technique can be used as a genericframework with any scheduling strategy. In this paper, Genetic Algorithm-based(GA-based) multi- objective scheduler is considered to illustrate theefficiency of the proposed adaptive scheduling solution. Results show thatusing the combination of clustering and classification algorithms along withthe GA optimizes the GA scheduler functionality and makes use of the ML processto form a closed loop scheduling mechanism.

Adaptive Closed Loop OFDM-Based Resource Allocation Method using Machine Learning and Genetic Algorithm