• Journal of Terahertz Science and Electronic Information Technology
  • Vol. 19, Issue 4, 573 (2021)
TONG Le*, LIANG Tao, ZHANG Yu, and QIAN Pengzhi
Author Affiliations
  • [in Chinese]
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    DOI: 10.11805/tkyda2021172 Cite this Article
    TONG Le, LIANG Tao, ZHANG Yu, QIAN Pengzhi. Dynamic spectrum allocation method based on multi-agent reinforcement learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(4): 573 Copy Citation Text show less
    References

    [1] DTA B , AB B,MD A,et al. Survey on spectrum sharing/allocation for cognitive radio networks Internet of Things[J]. Egyptian

    [2] ZHANG Z,XIAO Y,MA Z,et al. 6G wireless networks:vision,requirements,architecture,and key technologies[J]. IEEE

    [3] DU B,XUE R,ZHAO L,et al. Coalitional graph game for air-to-air and air-to-ground cognitive spectrum sharing[J]. IEEE

    [4] FARSHBAFAN M K,BAHOAR M H,KHAIEHRAVENI F. Spectrum trading for Device-to-Device communication in cellular

    [5] BHATTARAI S,PARK J M,LEHR W. Dynamic exclusion zones for protecting primary users in database-driven spectrum sharing[J]. IEEE/ACM Transactions on Networking, 2020,28(4):1506-1519.

    [6] KARMAKAR R,CHATTOPADHYAY S,CHAKRABORTY S. Dynamic link adaptation in IEEE 802.11 ac:a distributed learning based approach[C]// IEEE 41st Conference on Local Computer Networks(LCN). Dubai:IEEE, 2016:87-94.

    [7] HE J,PENG J,JIANG F,et al. A distributed Q learning spectrum decision scheme for cognitive radio sensor network[J]. International Journal of Distributed Sensor Networks, 2015,11(5):1-10.

    [8] OYEWOBI S S,HANCKE G P,ABU-MAHFOUZ A M,et al. An effective spectrum handoff based on reinforcement learning for target channel selection in the industrial internet of things[J]. Sensors, 2019,19(6):1395-1416.

    [9] KOUSHIK A M,HU F,KUMAR S. Intelligent spectrum management based on transfer actor-critic learning for rateless transmissions in cognitive radio networks[J]. IEEE Transactions on Mobile Computing, 2017,17(5):1204-1215.

    [10] BAIRAGI A K,ABEDIN S F,TRAN N H,et al. QoE-enabled unlicensed spectrum sharing in 5G:a game-theoretic approach[J]. IEEE Access, 2018(6):50538-50554.

    [11] LUONG N C,HOANG D T,GONG S,et al. Applications of deep reinforcement learning in communications and networking: a survey[J]. IEEE Communications Surveys & Tutorials, 2019,21(4):3133-3174.

    [12] MITOLA J. Cognitive radio for flexible mobile multimedia communications[C]// IEEE International Workshop on Mobile Multimedia Communications. San Diego,CA,USA:IEEE, 1999:3-10.

    [14] ZHOU L,WANG X,TU W,et al. Distributed scheduling scheme for video streaming over multi-channel multi-radio multi-hop wireless networks[J]. IEEE Journal on Selected Areas in Communications, 2010,28(3):409-419.

    [15] KHAN S,DUHOVNIKOV S,STEINBACH E,et al. MOS-based multiuser multiapplication cross-layer optimization for mobile multimedia communication[J]. Advances in Multimedia, 2007:6.

    [16] HE L,LIU B,YAO Y,et al. MOS-based channel allocation schemes for mixed services over cognitive radio networks[C]// 2013 Seventh International Conference on Image and Graphics. Qingdao,China:IEEE, 2013:832-837.

    [17] VLASSIS N. A concise introduction to multi-agent systems and distributed artificial intelligence[J]. Synthesis Lectures on Artificial Intelligence and Machine Learning, 2007,1(1):1-71.

    [18] SHIN M,CHUNG M Y. Learning-based distributed multi-channel dynamic access for cellular spectrum sharing of multiple operators[C]// 25th Asia-Pacific Conference on Communications(APCC). Haiphong.Vietnam:IEEE, 2019:384-387.

    [19] JIANG H,WANG T,WANG S. Multi-agent reinforcement learning for dynamic spectrum access[C]// IEEE International Conference on Communications(ICC). Shanghai,China:IEEE, 2019:1-6.

    TONG Le, LIANG Tao, ZHANG Yu, QIAN Pengzhi. Dynamic spectrum allocation method based on multi-agent reinforcement learning[J]. Journal of Terahertz Science and Electronic Information Technology , 2021, 19(4): 573
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