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Distributed Spectrum and Power Allocation for D2D-U Networks: a Scheme Based on NN and Federated Learning

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Abstract

In this paper, a Device-to-Device communication on unlicensed bands (D2D-U) enabled network is studied. To improve the spectrum efficiency (SE) on the unlicensed bands and fit its distributed structure while ensuring the fairness among D2D-U links and the harmonious coexistence with WiFi networks, a distributed joint power and spectrum scheme is proposed. In particular, a parameter, named as price, is defined, which is updated at each D2D-U pair by a online trained Neural network (NN) according to the channel state and traffic load. In addition, the parameters used in the NN are updated by two ways, unsupervised self-iteration and federated learning, to guarantee the fairness and harmonious coexistence. Then, a non-convex optimization problem with respect to the spectrum and power is formulated and solved on each D2D-U link to maximize its own data rate. Numerical simulation results are demonstrated to verify the effectiveness of the proposed scheme.

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References

  1. Zou Z, Yin R, Wu C, Yuan J, Chen X (2020) Distributed spectrum and power allocation for D2D-U networks. EAI MONAMI

  2. Doppler K, Rinne M, Wijting C, Ribeiro C, Hugl K (2009) Deviceto-device communication as an underlay to LTE-advanced networks. IEEE Commun Mag 47(12):42–49

    Article  Google Scholar 

  3. Yu G, Xu L, Feng D, Yin R, Li G, Jiang Y (2014) Joint mode selection and resource allocation for device-to-device communications. IEEE Trans Commun 62(11):3814–3824

    Article  Google Scholar 

  4. Wu Y, Guo W, Yuan H, Li L, Wang S, Chu X, Zhang J (2016) Device-to-device meets lte-unlicensed. IEEE Commun Mag 54(5):154–159

    Article  Google Scholar 

  5. Liu R, Yu G, Qu F, Zhang Z (2016) Device-to-device communications in unlicensed spectrum: mode selection and resource allocation. IEEE ACCESS 4:4720–4729

    Article  Google Scholar 

  6. Shah S, Rahman M, Mian A, Imran A, Mumtaz S, Dobre O (2019) On the impact of mode selection on effective capacity of device-to-device communication. IEEE Wireless Commun Letters 8 (3):945–948

    Article  Google Scholar 

  7. Yu G, Xu L, Feng D, Yin R, Li G (2014) Joint mode selection and resource allocation for device-to-device communications. IEEE Trans Commun 62(11):3814–3824

    Article  Google Scholar 

  8. Yin R, Zhong C, Yu G, Zhang Z, Wong K, Chen X (2016) Joint spectrum and power allocation for D2D communications underlaying cellular networks. IEEE Trans Veh Technol 65(4):2182–2195

    Article  Google Scholar 

  9. Yin R, Yu G, Zhang H, Zhang Z, Li G (2015) Pricing-based Interference coordination for D2D communications in cellular networks. IEEE Trans Wireless Commun 14(3):1519– 1532

    Article  Google Scholar 

  10. Lee W, Kim M, Cho D (2019) Transmit power control using deep neural network for underlay device-to-device communication. IEEE Commun Letters 8(1):141–144

    Article  Google Scholar 

  11. Moussaid A, Jaafar W, Ajib W, Elbiaze H (2018) Deep reinforcement learning-based data transmission for d2d communications. IEEE Int Conf WiMob: 1–7 https://ieeexplore.ieee.org/document/8589114

  12. QualComm (2013) Extending LTE advanced to unlicensed spectrum, White Paper, San Diego, CA, USA

  13. Huawei (2014) U-LTE: unlicensed spectrum utilization of LTE, White Paper, Shenzhen, China

  14. Study on licensed-assisted access to unlicensed spectrum (Release 13), document 3GPP, Sophia Antipolis Cedex, France, TR 36.889 (2015)

  15. Sun X, Dai L (2020) Towards fiar and efficient spectrum sharing between LTE and WiFi in unlicensed bands: fairness-constrained throughput maximization. IEEE Trans Wireless Commun 19(4):2713–2727. Early Access

    Article  Google Scholar 

  16. Cui Q, Ni W, Li S, Zhao B, Liu R, Zhang P (2020) Learning-assisted clustered access of 5G/B5G networks to unlicensed spectrum. IEEE Wireless Commun 27(1):31–37

    Article  Google Scholar 

  17. Yin R, Yu G, Maaref A, Li G (2016) LBT-Based adaptive channel access for LTE-u systems. IEEE Trans Wireless Commun 15(10):6585–6597

    Article  Google Scholar 

  18. Maglogiannis V, Naudts D, Shahid A, Moerman I (2018) A Q-Learning scheme for fair coexistence between LTE and Wi-Fi in unlicensed spectrum. IEEE Access 6:27278–27293

    Article  Google Scholar 

  19. Santana P, Sousa V, Abinader F, Neto J (2019) DM-CSAT: a LTE-u/wi-fi coexistence solution based on reinforcement learning. Telecommun Syst 71(4):615–626

    Article  Google Scholar 

  20. Neto J, Neto S, Santana P, Sousa V (2020) Multi-cell LTE-u/wi-fi coexistence evaluation using a reinforcement learning framework. Sensors 20(7):1855–1877

    Article  Google Scholar 

  21. Liu S, Yin R, Yu G (2019) Hybrid adaptive channel access for LTE-U systems. IEEE Trans Veh Technol 68(10):9820–9832

    Article  Google Scholar 

  22. Andreev S, Galinina O, Pyataev A, Johnsson K, Koucheryavy Y (2015) Analyzing assisted offloading of cellular user sessions onto D2D links in unlicensed bands. IEEE J Selected Areas in Commun 33 (1):67–80

    Article  Google Scholar 

  23. Zhang H, Liao Y, Song L (2017) D2D-U: device-to-device communications in unlicensed bands for 5G system. IEEE Trans Wireless Commun 16(6):3507–3519

    Article  Google Scholar 

  24. Zou Z, Yin R, Chen X, Wu C (2019) Deep reinforcement learning for D2D transmission in unlicensed bands. IEEE/CIC ICCC Workshops: 42–47 https://ieeexplore.ieee.org/document/8849971

  25. Yin R, Wu Z, Liu S, Wu C, Yuan J, Chen X Decentralized radio resource adaptation in D2D-U networks. IEEE Internet of Things Journal. https://doi.org/10.1109/JIOT.2020.3016019

  26. Konecny J, Brendan McMahan H, Yu F, Richtarik P, Suresh A, Bacon D (2017) Federated learning: strategies for improving communication efficiency. arXiv:11610.05492

  27. Niknam S, Dhillon HS, Reed JH (2019) Federated learning for wireless communications: motivation, opportunities and challenges. arXiv:1908.06847

  28. Bianchi G, Tinnirello I (2003) Kalman filter estimation of the number of competing terminals in an IEEE 802.11 network. IEEE INFOCOM 2:844–852

    Google Scholar 

  29. Qin F, Dai X, Mitchell J (2013) Effective-SNR estimation for wireless sensor network using Kalman filter. Ad Hoc Netw 11(3):944–958

    Article  Google Scholar 

  30. Yin R, Zou Z, Wu C, Yuan J, Chen X, Yu G (2020) Learning-based WiFi traffic load estimation in NR-U systems. IEICE Trans. https://doi.org/10.1587/transfun.2020EAP1063,

  31. Bianchi G (2000) Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J Sel Areas Commun 18(3):535–547

    Article  Google Scholar 

  32. Yang Q, Liu Y, Chen T, Tong Y (2019) Federated machine learning: concept and applications. ACM Trans Intell Syst Technol 10(2):1–19

    Article  Google Scholar 

  33. Zhou Z, Wang B, Gu B, Ai B, Mumtaz S, Rodriguez J (2020) Time-dependent pricing for bandwidth slicing under information asymmetry and price discrimination. IEEE Trans Commun 68 (11):6975–6989

    Article  Google Scholar 

  34. Gu B, Yang X, Lin Z, Hu W, Alazab M, Kharel R (2020) Multi-agent actor-critic network-based incentive mechanism for mobile crowdsensing in industrial systems. IEEE Trans Ind Inform

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant No. 61771429, No. 61703368, in part by Zhejiang University City College Scientific Research Foundation under Grant No. JZD18002, in part by Zhejiang Provincial Key Laboratory of Information Processing, Communication and Networking, and in part by the selective Grants for Postdoctoral Programs ZJ2020035 in Zhejiang Province, in part by ROIS NII Open Collaborative Research 2020-20S0502, and JSPS KAKENHI grant numbers 18KK0279, 19H04093 and 20H00592.

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Authors and Affiliations

  1. School of Information and Electrical Engineering, Zhejiang University City College, Hangzhou, 310015, China

    Rui Yin

  2. College of Information Science and Electrical Engineering, Zhejiang University, Hangzhou, China

    Zhiqun Zou

  3. Graduate School of Informatics and Engineering, The University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585, Japan

    Celimuge Wu

  4. Institute of Ocean Sensing and Networking of the Ocean College, Zhejiang University, Hangzhou, China

    Jiantao Yuan

  5. VTT Technical Research Centre of Finland, Otaniemi, Finland

    Xianfu Chen

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  1. Rui Yin
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  2. Zhiqun Zou
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  3. Celimuge Wu
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Correspondence to Celimuge Wu.

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The article is an extended version of MONAMI 2020 conference paper [1].

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Yin, R., Zou, Z., Wu, C. et al. Distributed Spectrum and Power Allocation for D2D-U Networks: a Scheme Based on NN and Federated Learning. Mobile Netw Appl 26, 2000–2013 (2021). https://doi.org/10.1007/s11036-021-01736-2

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  • DOI: https://doi.org/10.1007/s11036-021-01736-2

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