Quality of service based radar resource management using deep reinforcement learning

10/20/2020
by   Sebastian Durst, et al.
0

An intelligent radar resource management is an essential milestone in the development of a cognitive radar system. The quality of service based resource allocation model (Q-RAM) is a framework allowing for intelligent decision making but classical solutions seem insufficient for real-time application in a modern radar system. In this paper, we present a solution for the Q-RAM radar resource management problem using deep reinforcement learning considerably improving on runtime performance.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/18/2021

Model-Based Reinforcement Learning Framework of Online Network Resource Allocation

Online Network Resource Allocation (ONRA) for service provisioning is a ...
research
06/17/2021

Modelling resource allocation in uncertain system environment through deep reinforcement learning

Reinforcement Learning has applications in field of mechatronics, roboti...
research
11/19/2019

Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning

This paper considers the problem of resource allocation in stream proces...
research
11/25/2019

Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem

Order dispatching and driver repositioning (also known as fleet manageme...
research
03/03/2023

Study on the Data Storage Technology of Mini-Airborne Radar Based on Machine Learning

The data rate of airborne radar is much higher than the wireless data tr...
research
05/11/2023

Optimizing Memory Mapping Using Deep Reinforcement Learning

Resource scheduling and allocation is a critical component of many high ...
research
08/17/2022

Autonomous Resource Management in Construction Companies Using Deep Reinforcement Learning Based on IoT

Resource allocation is one of the most critical issues in planning const...

Please sign up or login with your details

Forgot password? Click here to reset