Name
Title | ||
|---|---|---|
Reinforcement Learning Based Predictive Handover For Pedestrian-Aware Mmwave Networks |
Abstract | ||
|---|---|---|
This paper discusses the optimal decision-making for predictive handover in millimeter-wave (mmWave) communication networks using information of pedestrian movement. In mmWave communication networks, human blockage causes significant performance degradation. Hence, to maximize the throughput, it might be important to perform a handover predictively using information such as location and velocity of a pedestrian. To optimize the timing to perform the predictive handover, this paper presents a reinforcement learning framework. The important point in this framework is learning the optimal handover policy maximizing the future throughput expected under the locations and velocities of a pedestrian. To learn the optimal policy, this paper applies Q-learning. The numerical results demonstrate that the learned handover decisions outperform the heuristic handover decisions in terms of throughput performance. |
Year | Venue | Field |
|---|---|---|
2018 | IEEE INFOCOM 2018 - IEEE CONFERENCE ON COMPUTER COMMUNICATIONS WORKSHOPS (INFOCOM WKSHPS) | Heuristic,Pedestrian,Telecommunications network,Computer science,Computer network,Throughput,Handover,Reinforcement learning |
DocType | ISSN | Citations |
Conference | 2159-4228 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yusuke Koda | 1 | 4 | 3.78 |
| Koji Yamamoto | 2 | 135 | 45.58 |
| Takayuki Nishio | 3 | 106 | 38.21 |
| Masahiro Morikura | 4 | 184 | 63.42 |