The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. This paper studies the influence of the blockage in pedestrian scenario, explains in detail how blockage affects the mmWave propagation characteristics. In particular, we study the behavior of the blockage due to human mobility and how it affects the timescale for outage due to blockage using knife-edge diffraction model (KED). One of the existing solutions to overcome the influence of blockage, is to associate the user equipment (UE) with other available base stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as blockage-aware user association (BAUA). The other focuses on the reward to balance the trade-off between the throughput and the LoS probability and is named as modified BAUA. We compare the proposed algorithm with the conventional user algorithms such as the maximum throughput based algorithm and the maximum SINR based algorithm. Simulation results show that to increase the sum LoS probability BAUA would be suitable, and to increase the average throughput maximum throughput based method would be suitable.
ASJC Scopus subject areas