Deep Reinforcement Learning for Cognitive Radar Spectrum Sharing: A Continuous Control Approach

被引：6
|
作者：
Flandermeyer, Shane A. [1 ]
Mattingly, Rylee G. [1 ]
Metcalf, Justin G. [1 ]
机构：
[1] The University of Oklahoma, Advanced Radar Research Center (ARRC), The Department of Electrical and Computer Engineering (ECE), Norman,OK,73072, United States
来源：
IEEE Transactions on Radar Systems | 2024年 / 2卷
关键词：
The growing demand for RF spectrum has placed considerable strain on radar systems; which must share limited spectrum resources with an ever-increasing number of devices. It is necessary to design radar systems with coexistence in mind so that the radar avoids harmful mutual interference that compromises the quality of service for other users in the channel. This work presents a deep reinforcement learning (RL) approach to spectrum sharing that enables a pulse-agile radar to operate in heavily congested spectral environments. A cognitive agent dynamically adapts the radar waveform to trade off collision avoidance; bandwidth utilization; and distortion losses due to pulse-agile behavior. Unlike existing RL approaches; this method formulates waveform parameter selection as a continuous control task; significantly increasing the flexibility of the agent and making it possible to scale its behavior to wideband; high-resolution operation. The RL agent uses a recurrent attention-based neural network to select actions; making it suitable for parallelized; real-time implementation. The proposed algorithm makes minimal assumptions about the spectral environment or other users in the spectrum; and the performance of the approach is evaluated on over-the-air data collected from a USRP X310 software-defined radio (SDR) system. Through these experiments; it is shown that the RL approach provides a flexible method for solving multi-objective waveform design problems in dynamic; high-dimensional spectrum environments. © 2023 IEEE;
D O I：
10.1109/TRS.2024.3353112
中图分类号：
学科分类号：
摘要：
引用
收藏
页码：125 / 137
相关论文
共 50 条
  • [1] Intelligent Power Control for Spectrum Sharing in Cognitive Radios: A Deep Reinforcement Learning Approach
    Li, Xingjian
    Fang, Jun
    Cheng, Wen
    Duan, Huiping
    Chen, Zhi
    Li, Hongbin
    IEEE ACCESS, 2018, 6 : 25463 - 25473
  • [2] Smooth Deep Reinforcement Learning for Power Control for Spectrum Sharing in Cognitive Radios
    Dang, Lujuan
    Wang, Wanli
    Tse, Chi K. K.
    Lau, Francis C. M.
    Wang, Shiyuan
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2022, 21 (12) : 10621 - 10632
  • [3] Power Control Based on Deep Reinforcement Learning for Spectrum Sharing
    Zhang, Haijun
    Ning Yang
    Wei Huangfu
    Long, Keping
    Leung, Victor C. M.
    IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (06) : 4209 - 4219
  • [4] Experimental Analysis of Reinforcement Learning Techniques for Spectrum Sharing Radar
    Thornton, Charles E.
    Buehrer, R. Michael
    Martone, Anthony F.
    Sherbondy, Kelly D.
    2020 IEEE INTERNATIONAL RADAR CONFERENCE (RADAR), 2020, : 67 - 72
  • [5] An IOV Spectrum Sharing Approach based on Multi-Agent Deep Reinforcement Learning
    Qian, Haizhong
    Cai, Lili
    INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS, 2024, 32 (04) : 571 - 592
  • [6] Deep Reinforcement Learning for UAV-Assisted Spectrum Sharing: A Minority Game Approach
    Ding, Xinyu
    Zhang, Jie
    Wang, Zhe
    Wang, Xuehe
    2023 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS, ITHINGS IEEE GREEN COMPUTING AND COMMUNICATIONS, GREENCOM IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING, CPSCOM IEEE SMART DATA, SMARTDATA AND IEEE CONGRESS ON CYBERMATICS,CYBERMATICS, 2024, : 44 - 50
  • [7] When Spectrum Sharing in Cognitive Networks Meets Deep Reinforcement Learning: Architecture, Fundamentals, and Challenges
    Si, Jiangbo
    Huang, Rui
    Li, Zan
    Hu, Hang
    Jin, Yuntao
    Cheng, Julian
    Al-Dhahir, Naofal
    IEEE NETWORK, 2024, 38 (01): : 187 - 195
  • [8] Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR
    Challita, Ursula
    Sandberg, David
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [9] Benchmarking Deep Reinforcement Learning for Continuous Control
    Duan, Yan
    Chen, Xi
    Houthooft, Rein
    Schulman, John
    Abbeel, Pieter
    INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 48, 2016, 48
  • [10] Dynamic Spectrum Sharing and Aggregation Scheme Based on Deep Reinforcement Learning
    Sheng, Tianqi
    Zhang, Wensheng
    Ding, Wenjiao
    Sun, Jian
    Wang, Cheng-Xiang
    2022 INTERNATIONAL WIRELESS COMMUNICATIONS AND MOBILE COMPUTING, IWCMC, 2022, : 290 - 294
12345