Reinforcement Learning-based Joint Handover and Beam Tracking in Millimeter-wave Networks

IEEE Wireless Communications and Networking Conference, pp. 1–6

Abstract

In this paper, we develop an algorithm for joint handover and beam tracking in millimeter-wave (mmWave) networks. The aim is to provide a reliable connection in terms of the achieved throughput along the trajectory of the mobile user while preventing frequent handovers. We model the association problem as an optimization problem and propose a reinforcement learning-based solution. Our approach learns whether and when beam tracking and handover should be performed and chooses the target base stations. In the case of beam tracking, we propose a tracking algorithm based on measuring a small spatial neighbourhood of the optimal beams in the previous time slot. Simulation results in an outdoor environment show the superior performance of our proposed solution in achievable throughput and the number of handovers needed in comparison to a multi-connectivity baseline and a learning-based handover baseline.

Authors 3

1. Sara Khosravi

   KTH Royal Institute of Technology

   Affiliation as printed

   KTH Royal Institute of the Technology,School of EECS,Stockholm,Sweden

   School of EECS, KTH Royal Institute of the Technology, Stockholm, Sweden

2. Hossein Shokri‐Ghadikolaei Aachen Mobile Communications and Computing (MCC)

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Mobile Communications and Computing,Germany

   Mobile Communications and Computing, RWTH Aachen University, Germany

3. Jens Zander

   KTH Royal Institute of Technology

   Affiliation as printed

   KTH Royal Institute of the Technology,School of EECS,Stockholm,Sweden

   School of EECS, KTH Royal Institute of the Technology, Stockholm, Sweden