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Tytuł artykułu

Analysis of an LSTM-based NOMA Detector Over Time Selective Nakagami-m Fading Channel Conditions

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
This work examines the efficacy of deep learning (DL) based non-orthogonal multiple access (NOMA) receivers in vehicular communications (VC). Analytical formulations for the outage probability (OP), symbol error rate (SER), and ergodic sum rate for the researched vehicle networks are established Rusing i.i.d. Nakagami-m fading links. Standard receivers, such as least square (LS) and minimum mean square error (MMSE), are outperformed by the stacked long-short term memory (S-LSTM) based DL-NOMA receiver. Under real time propagation circumstances, including the cyclic prefix (CP) and clipping distortion, the simulation curves compare the performance of MMSE and LS receivers with that of the DL-NOMA receiver. According to numerical statistics, NOMA outperforms conventional orthogonal multiple access (OMA) by roughly 20% and has a high sum rate when considering i.i.d. fading links.
Rocznik
Tom
Strony
17--24
Opis fizyczny
Bibliogr. 24 poz., rys., tab.
Twórcy
autor
  • Madanapalle Institute of Technology and Science, Madanapalle, Andhra Pradesh, India
  • Cummins College of Engineering for Women, Pune, India
autor
  • Department of Computer Science and Engineering, Jecrc University, Jaipur, India
  • Electrical & Electronics Engineering Department, Eklavya University, Sagar Road, Damoh, India
  • Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, Rajouri, J&K, India
  • Department of Information Technology, Sinhgad Academy of Engineering, Pune, India
Bibliografia
  • [1] T. Xu, C. Xu, and Z. Xu, “An efficient three-factor privacy-preserving authentication and key agreement protocol for vehicular ad-hoc network”, in China Communications, vol. 18, no. 12, pp. 315–331, 2021 (DOI: 10.23919/JCC.2021.12.020).
  • [2] L.-L.Wang, J.-S. Gui, X.-H. Deng, F. Zeng, and Z.-F. Kuang, “Routing Algorithm Based on Vehicle Position Analysis for Internet of Vehicles”, in IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11701–11712, 2020 (DOI: 10.1109/JIOT.2020.2999469).
  • [3] F. Zhu, et al., “Parallel Transportation Systems: Toward IoT-Enabled Smart Urban Traffic Control and Management”, in IEEE Transactions on Intelligent Transportation Systems, vol. 21, no. 10, pp. 4063–4071, 2020 (DOI: 10.1109/TITS.2019.2934991).
  • [4] P. K. Singh, S. K. Nandi, and S. Nandi, “A tutorial survey on vehicular communication state of the art, and future research directions”, Vehicular Communications, vol. 18, Article ID 100164, 2019 (ISSN 2214–2096, DOI: 10.1016/j.vehcom.2019.100164).
  • [5] A. Kumar, S. Majhi, and H.-C. Wu, “Physical-Layer Security of Underlay MIMO-D2D Communications by Null Steering Method Over Nakagami-m and Norton Fading Channels”, in IEEE Transactions on Wireless Communications (DOI: 10.1109/TWC.2022.3178758).
  • [6] BP. Chaudhary, R. Shankar, and RK. Mishra. “A tutorial on cooperative non-orthogonal multiple access networks”, The Journal of Defense Modeling, and Simulation, 2021 (DOI:10.1177/1548512920986627).
  • [7] L. Bhardwaj, RK. Mishra, and R. Shankar, “Investigation of lowdensity parity check codes concatenated multi-user massive multipleinput multiple-output systems with imperfect channel state information”, The Journal of Defense Modeling, and Simulation, vol. 19, no. 3, pp. 539–550, 2022 (DOI: 10.1177/1548512920968639).
  • [8] MK. Beuria, R. Shankar, and S. S. Singh, “Analysis of the energy harvesting non-orthogonal multiple access technique for defense applications over Rayleigh fading channel conditions”, The Journal of Defense Modeling, and Simulation, 2021 (DOI:10.1177/15485129211021168).
  • [9] R. Tiwari and S. Deshmukh, “Prior information-based Bayesian MMSE estimation of velocity in HetNets”, IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 81–84, 2018 (DOI:10.1109/LWC.2018.2857805).
  • [10] R. Tiwari and S. Deshmukh, “Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets”, Transactions on Emerging Telecommunications Technologies, vol. 33, no. 3, 2022 (DOI: 10.1002/ett.3642).
  • [11] R. Tiwari and S. Deshmukh, “Handover count based MAP estimation of velocity with prior distribution approximated via NGSIM data-set”, IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4352–4361, 2021 (DOI: 10.1109/TITS.2020.3043888).
  • [12] S. Wong, et al., “Traffic forecasting using vehicle-to-vehicle communication”, 3rd Annual Conference on Learning for Dynamics and Control, pp. 917–929, 2021 (https://arxiv.org/pdf/2104.05528).
  • [13] C. Lin, Q. Chang, and X. Li, “A Deep Learning Approach for MIMO-NOMA Downlink Signal Detection”, Sensors, vol. 19, p. 2526, 2019 (DOI: 10.3390/s19112526).
  • [14] J. M. Kang, I. M. Kim, and C. J. Chun, “Deep Learning-Based MIMO-NOMA With Imperfect SIC Decoding”, in IEEE Systems Journal, vol. 14, no. 3, pp. 3414–3417, 2020 (DOI:10.1109/JSYST.2019.2937463).
  • [15] R. Malladi, M. K. Beuria, R. Shankar, and S. S. Singh, “Investigation of the fifth generation non-orthogonal multiple access technique for defense applications using deep learning”, The Journal of Defense Modeling, and Simulation, 2021 (DOI: 10.1177/15485129211022857).
  • [16] X. Gong, X. Yue, and F. Liu, “Performance Analysis of Cooperative NOMANetworks with Imperfect CSI over Nakagami-mFading Channels”, Sensors, vol. 20, no. 2, p. 424, 2021 (DOI: 10.3390/s20020424).
  • [17] Narengerile and J. Thompson, “Deep Learning for Signal Detection in Non-Orthogonal Multiple Access Wireless Systems”, 2019 UK/China Emerging Technologies (UCET), pp. 1–4, 2019 (DOI:10.1109/UCET.2019.8881888).
  • [18] R. Shankar, T. V. Ramana, P. Singh, S. Gupta, and H. Mehraj, “Examination of the Non-Orthogonal Multiple Access System Using Long Short Memory Based Deep Neural Network”, Journal of Mobile Multimedia, vol. 18, no. 2, pp. 451–474, 2021 (DOI:10.13052/jmm1550-4646.18214).
  • [19] M. AbdelMoniem, S. M. Gasser, M. S. El-Mahallawy, M. W. Fakhr, and A. Soliman. 2019. “Enhanced NOMA System Using Adaptive Coding and Modulation Based on LSTM Neural Network Channel Estimation“, Applied Sciences, vol. 9, no. 15, Article ID 3022, 2019 (DOI: 10.3390/app9153022).
  • [20] M. A. Ahmed, A. Baz, and C. C. Tsimenidis, “Performance analysis of NOMA systems over Rayleigh fading channels with successive-interference cancellation”, IET Communications 14, no. 6 pp. 1065–1072, 2020 (DOI: 10.1049/iet-com.2019.0504).
  • [21] S. Mukhtar and GR. Begh, “Error analysis of TAS-OSTBC assisted downlink NOMA system over generalized η − μ η − μ fading Channel”, International Journal of Communication Systems, e5234 (DOI: 10.1002/dac.5234).
  • [22] D. K. Patel, et al., “Performance Analysis of NOMA in Vehicular Communications Over i.n.i.d. Nakagami-m Fading Channels”, In IEEE Transactions on Wireless Communications, vol. 20, no. 10, pp. 6254–6268, 2021 (DOI: 10.1109/TWC.2021.3073050).
  • [23] A. Saxena Vehicle-to-Vehicle Communication: Let the car Messager while driving, not you! eInfochips, an Arrow company, (https://www.einfochips.com/blog/vehicle-to-vehiclecommunication-let-the-car-message-while-driving-not-you/).
  • [24] S. Barmpounakis, et al., “LSTM-based QoS prediction for 5G-enabled Connected and Automated Mobility applications”, IEEE 4th 5G World Forum (5GWF), pp. 436–440, 2021 (DOI:10.1109/5GWF52925.2021.00083).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fb187d69-820f-4f25-8f14-005cb7e2e3e4
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