Paper Info
Title
Cooperative Sensing in Deep RL-Based Image-to-Decision Proactive Handover for mmWave Networks
Abstract
For reliable millimeter-wave (mmWave) networks, this paper proposes cooperative sensing with multi-camera operation in an image-to-decision proactive handover framework that directly maps images to a handover decision. In the framework, camera images are utilized to allow for the prediction of blockage effects in a mmWave link, whereby a network controller triggers a handover in a proactive fashion. Furthermore, direct mapping allows for the scalability of the number of pedestrians. This paper experimentally investigates the feasibility of adopting cooperative sensing with multiple cameras that can compensate for one another's blind spots. The optimal mapping is learned via deep reinforcement learning to resolve the high dimensionality of images from multiple cameras. An evaluation based on experimentally obtained images and received powers verifies that a mapping that enhances channel capacity can be learned in a multi-camera operation. The results indicate that our proposed framework with multi-camera operation outperforms a conventional framework with single-camera operation in terms of the average capacity.
Year
DOI
Venue
2020
10.1109/CCNC46108.2020.9045186
2020 IEEE 17th Annual Consumer Communications & Networking Conference (CCNC)
Keywords
DocType
ISSN
deep RL-based image-to-decision proactive handover,reliable millimeter-wave networks,multicamera operation,camera images,mmWave link,network controller,direct mapping,optimal mapping,deep reinforcement learning,cooperative sensing,blockage effects,pedestrians,channel capacity
Conference
2331-9852
ISBN
Citations 
PageRank 
978-1-7281-3894-7
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Yusuke Koda172.84
Kota Nakashima282.49
Koji Yamamoto38616.61
Takayuki Nishio410638.21
Masahiro Morikura518463.42