Name
Title | ||
|---|---|---|
Deep Reinforcement Learning-Based Beam Tracking From Mmwave Antennas Installed On Overhead Messenger Wires |
Abstract | ||
|---|---|---|
To achieve reliable small cell millimeter-wave wireless backhauls, this study installs small cell base stations (SBSs) on overhead messenger wires to gain flexibility in physical deployments of SBSs ensuring in the line-of-sight connections between SBSs and gateway BSs. These installations pose challenges in aligning directional beams, whereby complicated wind forced dynamics in on-wire SBSs require frequent beam training, and consequently, a large signaling overhead. To address this, this study aims at demonstrating the feasibility of learning based beam tracking where a beam tracking policy is learned a priori to fix beam misalignment caused by the wind-forced dynamics. Because wind-forced dynamics in SBSs can be three-dimensional (3D), the proposed beam tracking newly exploits the 3D position/velocity of the SBS as state information. As a solution to fix beam misalignment, the beam tracking policy is learned via deep reinforcement learning wherein the 3D information and beam direction are regarded as a state and an action, respectively, and the received signal power at a gateway BS is maximized The simulation results depict the feasibility of learning an appropriate beam tracking policy to prevent beam misalignment induced by wind-forced 3D dynamics in on-wire SBSs. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/VTC2020-Fall49728.2020.9348475 | 2020 IEEE 92ND VEHICULAR TECHNOLOGY CONFERENCE (VTC2020-FALL) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Masao Shinzaki | 1 | 0 | 0.34 |
| Yusuke Koda | 2 | 7 | 2.84 |
| Koji Yamamoto | 3 | 0 | 1.69 |
| Takayuki Nishio | 4 | 106 | 38.21 |
| Masahiro Morikura | 5 | 184 | 63.42 |
| Chun-Hsiang Huang | 6 | 0 | 0.34 |
| Yushi Shirato | 7 | 0 | 0.34 |
| Naoki Kita | 8 | 0 | 0.34 |