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Integrating unmanned aerial vehicles (UAVs) into existing cellular networksencounters lots of challenges, among which one of the most striking concerns ishow to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs,and terrestrial user equipments (UEs). In this paper, a cellular-connected UAVnetwork is focused, where multiple UAVs receive messages from base stations(BSs) in the down-link, while BSs are serving ground UEs in their cells. Foreffectively managing inter-cell interferences (ICIs) among UEs due to intensereuse of time-frequency resource block (RB) resource, a first $p$-tier based RBcoordination criterion is proposed and adopted. Then, to enhance wirelesstransmission quality for UAVs while protecting terrestrial UEs from beinginterfered by ground-to-air (G2A) transmissions, a radio resource management(RRM) problem of joint dynamic RB coordination and time-varying beamformingdesign minimizing UAV's ergodic outage duration (EOD) is investigated. To copewith conventional optimization techniques' inefficiency in solving theformulated RRM problem, a deep reinforcement learning (DRL)-aided solution isinitiated, where deep double duelling Q network (D3QN) and twin delayed deepdeterministic policy gradient (TD3) are invoked to deal with RB coordination indiscrete action domain and beamforming design in continuous action regime,respectively. The hybrid D3QN-TD3 solution is trained via interacting with theconsidered outer and inner environments in an online centralized manner so thatit can then help achieve the suboptimal EOD minimization performance during itsoffline decentralized exploitation phase. Simulation results have illustratedthe effectiveness of the proposed hybrid D3QN-TD3 algorithm, compared toseveral representative baselines.

Radio Resource Management for Cellular-Connected UAV: A Learning Approach