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We consider a wireless network scenario applicable to metropolitan areas withdeveloped public transport networks and high commute demands, where the mobileuser equipments (UEs) move along fixed and predetermined trajectories andrequest to associate with millimeter-wave (mmWave) base stations (BSs). Aneffective and efficient algorithm, called the Sequence Q-learning Algorithm(SQA), is proposed to maximize the long-run average transmission rate of thenetwork, which is an NP-hard problem. Furthermore, the SQA tackles thecomplexity issue by only allowing possible re-associations (handover of a UEfrom one BS to another) at a discrete set of decision epochs and has polynomialtime complexity. This feature of the SQA also restricts too frequent handovers,which are considered highly undesirable in mmWave networks. Moreover, wedemonstrate by extensive numerical results that the SQA can significantlyoutperform the benchmark algorithms proposed in existing research by taking allUEs' future trajectories and possible decisions into account at every decisionepoch.

Sequence Q-Learning Algorithm for Optimal Mobility-Aware User Association