A Joint Radar and Communication Approach for 5G NR using Reinforcement Learning-Reference-Cited by-同舟云学术

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A Joint Radar and Communication Approach for 5G NR using Reinforcement Learning

Published:2023-05 Issue:5 Volume:61 Page:106-112
ISSN:0163-6804
Container-title:IEEE Communications Magazine
language:
Short-container-title:IEEE Commun. Mag.

Author:

Salami Dariush¹,Ning Wanru¹,Ruttik Kalle¹,Jäntti Riku¹,Sigg Stephan¹

Affiliation:

1. Aalto University,Finland

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Computer Science Applications

Link

http://xplorestaging.ieee.org/ielx7/35/10128972/10129041.pdf?arnumber=10129041

Reference17 articles.

1. Multi-Functional Coexistence of Radarsensing and Communication Waveforms;?ahin;The 2020 IEEE 92nd Veh Tech Conf (VTC2020-Fall),0

2. Multicarrier Radar-Communications Waveform Design for RF Convergence and Coexistence;bic?;ICASSP 2019 — 2019 IEEE Int'l Conf Acoustics Speech and Signal Processing (ICASSP),0

3. NR Wide Bandwidth Operations

4. Integrating Sensing and Communication in Cellular Networks via NR Sidelink;salami;Submitted to ACM J Trans Internet of Things,2022

5. Deep Neural Network Detection for Pulsed Radar-Embedded M-PSK Communications

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Coprime-Based Frame-Level Sensing OFDM Waveform for 5G NR Integrated Radar Sensing and Communication via Downlink Sensing;IEEE Sensors Journal;2024-08-01

2. Machine Learning Based Beam Selection for Maximizing Wireless Network Capacity;IEEE Access;2024

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