Learning-based Load Balancing Handover in Mobile Millimeter Wave Networks [Elektronisk resurs]

<p>QC 20210406</p>
gratis
Millimeter-wave (mmWave) communication is apromising solution to the high data rate demands in the upcom-ing 5G and beyond communication networks. When it comes tosupporting seamless connectivity in mobile scenarios, resourceand handover management are two of the main challengesin mmWave networks. In this paper, we address these twoproblems jointly and propose a learning-based load balancinghandover in multi-user mobile mmWave networks. Our handoveralgorithm selects a backup base station and allocates the resourceto maximize the sum rate of all the users while ensuringa target rate threshold and preventing excessive handovers.We model the user association as a non-convex optimizationproblem. Then, by applying a deep deterministic policy gradient(DDPG) method, we approximate the solution of the optimizationproblem. Through simulations, we show that our proposedalgorithm minimizes the number of the events where a user’srate is less than its minimum rate requirement and minimizesthe number of handovers while increasing the sum rate of allusers.

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