End-to-End Learning of Proactive Handover Policy for Camera-Assisted mmWave Networks Using Deep Reinforcement Learning

04/09/2019
by   Yusuke Koda, et al.
0

For mmWave networks, this paper proposes an image-to-decision proactive handover framework, which directly maps camera images to a handover decision. With the help of camera images, the proposed framework enables a proactive handover, i.e., a handover is triggered before a temporal variation in the received power induced by obstacles even if the variation is extremely rapid such that it cannot be predicted from a time series of received power. Furthermore, direct mapping allows scalability for the number of obstacles. This paper shows that optimal mapping is learned via deep reinforcement learning (RL) by proving that the decision process in our proposed framework is a Markov decision process. While performing deep RL, this paper designs a neural network (NN) architecture for a network controller to successfully learn the use of lower-dimensional observations in state information and higher-dimensional image observations. The evaluations based on experimentally obtained camera images and received powers indicate that the learned handover policy in the proposed framework outperforms the learned policy in a received power-based handover framework.

READ FULL TEXT

page 2

page 3

page 4

page 5

page 6

page 7

page 8

page 10

research
06/12/2019

Cooperative Sensing in Deep RL-Based Image-to-Decision Proactive Handover for mmWave Networks

For reliable millimeter-wave (mmWave) networks, this paper proposes coop...
research
02/19/2021

TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning

We propose a novel approach to interactive theorem-proving (ITP) using d...
research
10/21/2022

Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networks

The ability to direct a Probabilistic Boolean Network (PBN) to a desired...
research
07/09/2020

Learning to Prune Deep Neural Networks via Reinforcement Learning

This paper proposes PuRL - a deep reinforcement learning (RL) based algo...
research
10/26/2018

Deep-Reinforcement-Learning-Based Distributed Vehicle Position Controls for Coverage Expansion in mmWave V2X

In millimeter wave (mmWave) vehicular communications, multi-hop relay di...
research
03/08/2022

Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models

For applications in healthcare, physics, energy, robotics, and many othe...
research
04/01/2022

Building Decision Forest via Deep Reinforcement Learning

Ensemble learning methods whose base classifier is a decision tree usual...

Please sign up or login with your details

Forgot password? Click here to reset