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

TL;DR

This paper introduces a cooperative multi-camera sensing framework utilizing deep reinforcement learning for proactive handover in mmWave networks, improving channel capacity by predicting blockages and coordinating multiple cameras.

Contribution

It presents a novel multi-camera cooperative sensing approach with deep RL for proactive handover, enhancing scalability and performance in mmWave networks.

Findings

Multi-camera sensing improves blockage prediction accuracy.

Deep RL effectively learns optimal image-to-decision mapping.

Multi-camera operation outperforms single-camera in channel capacity.

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.