Examination of deep learning based NOMA system considering node mobility and imperfect CSI
DOI:
https://doi.org/10.3103/S0735272723070026Keywords:
symbol error rate, stacked long-term memory, multiple-input, multiple-output, non-orthogonal multiple access, deep neural network, deep learning, SER, S-LSTM, MIMO, anomalous errors, NOMAAbstract
This paper examines the efficiency of a downlink non-orthogonal multiple access (NOMA) system by using a deep learning (DL)-based stacked long short-term memory (S-LSTM) scheme. The vehicle-to-vehicle (V2V) channel is considered to be time-selective as a result of node mobility and the presence of imprecise channel state information (CSI). The use of the fifth generation (5G) tapped delay line type C (TDL-C) independent and identically distributed (IID) fading channel models allows for the production of channel taps that properly replicate the Nakagami-m fading wireless channel. The paper examines the outage probability (OP) and symbol error rate (SER) of both traditional and suggested channel estimators. It analyzes these metrics under various fading parameters, pilot symbols (PS), learning rate (LR), and batch size. The training of deep neural network (DNN) models is performed using the Adam optimizer. Enhancing the signal-to-noise ratio (SNR) may decrease the SER which results in the enhanced identification of the downlink channel in NOMA cell-based systems. Reducing the LR has a positive effect on the SER, validating the analytical findings that indicate greater changes in DNN weights and larger validation mistakes when the LR is raised. Nevertheless, this benefit is accompanied by the drawback of more frequent updates, resulting in a delay in the model’s convergence.
References
J. Borah, S. Baruah, S. Das, D. Biswas, “Analysis of massive MIMO and small cells based 5G cellular networks: Simulative approach,” Radioelectron. Commun. Syst., vol. 65, no. 6, pp. 284–292, 2022, doi: https://doi.org/10.3103/S0735272722060024.
D. O. Vasylenko, “Natural optimization algorithms in synthesis problems of built-in antennas of IoT devices (review),” Radioelectron. Commun. Syst., vol. 65, no. 3, pp. 111–128, 2022, doi: https://doi.org/10.3103/S0735272722030013.
J. Merin Joshiba, D. Judson, V. Bhaskar, “A comprehensive review on NOMA assisted emerging techniques in 5G and beyond 5G wireless systems,” Wirel. Pers. Commun., vol. 130, no. 4, pp. 2385–2405, 2023, doi: https://doi.org/10.1007/s11277-023-10384-6.
R. Shankar et al., “Impact of node mobility on the DL based uplink and downlink MIMO-NOMA network,” Int. J. Inf. Technol., vol. 15, no. 6, pp. 3391–3404, 2023, doi: https://doi.org/10.1007/s41870-023-01362-z.
J. Alanya-Beltran, R. Shankar, P. Krishna, S. Kumar S, “Investigation of bi-directional LSTM deep learning-based ubiquitous MIMO uplink NOMA detection for military application considering robust channel conditions,” J. Def. Model. Simul. Appl. Methodol. Technol., vol. 20, no. 2, pp. 229–244, 2023, doi: https://doi.org/10.1177/15485129211050403.
T. Hirai, N. Wakamiya, T. Murase, “NOMA-dependent low-powered retransmission in sensing-based SPS for cellular-V2X mode 4,” in 2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall), 2022, pp. 1–7, doi: https://doi.org/10.1109/VTC2022-Fall57202.2022.10012826.
T. Hirai, “Non-orthogonal multiple access for infrastructure-less cellular-V2X,” IEICE Tech. Rep., vol. 122, no. 48, pp. 38–42, 2022, uri: https://ken.ieice.org/ken/paper/202205272CK5/eng/.
S. Patel, D. Chauhan, S. Gupta, “An overview of non orthogonal multiple access for future radio communication,” in 2021 International Conference on Intelligent Technologies (CONIT), 2021, pp. 1–3, doi: https://doi.org/10.1109/CONIT51480.2021.9498336.
G. Liu, Z. Wang, J. Hu, Z. Ding, P. Fan, “Cooperative NOMA broadcasting/multicasting for low-latency and high-reliability 5G cellular V2X communications,” IEEE Internet Things J., vol. 6, no. 5, pp. 7828–7838, 2019, doi: https://doi.org/10.1109/JIOT.2019.2908415.
A. Ihsan, W. Chen, S. Zhang, S. Xu, “Energy-efficient NOMA multicasting system for beyond 5G cellular V2X communications with imperfect CSI,” IEEE Trans. Intell. Transp. Syst., vol. 23, no. 8, pp. 10721–10735, 2022, doi: https://doi.org/10.1109/TITS.2021.3095437.
R. Shankar, V. V. R. Raman, K. P. Rane, B. K. Sarojini, R. Neware, “An investigation of the MIMO space time block code based selective decode and forward relaying network over n-u fading channel conditions,” J. Telecommunictions Inf. Technol., vol. 1, no. 2022, pp. 79–92, 2022, doi: https://doi.org/10.26636/jtit.2022.150421.
R. Shankar, P. Krishna, R. Naraiah, “Examination of the multiple-input multiple-output space-time block-code selective decode and forward relaying protocol over non-homogeneous fading channel conditions,” J. Def. Model. Simul. Appl. Methodol. Technol., vol. 20, no. 2, pp. 245–258, 2023, doi: https://doi.org/10.1177/15485129211047598.
L. Han, W.-P. Zhu, M. Lin, “Outage performance of downlink coordinated direct and relay transmission with NOMA over Nakagami-m fading channels,” in 2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), 2020, pp. 1–6, doi: https://doi.org/10.1109/VTC2020-Fall49728.2020.9348857.
N. Jaiswal, A. Pandey, S. Yadav, N. Purohit, L. Bariah, S. Muhaidat, “On the performance of NOMA-enabled V2V communications under joint impact of nodes mobility and channel estimation error,” in 2021 4th International Conference on Advanced Communication Technologies and Networking (CommNet), 2021, pp. 1–9, doi: https://doi.org/10.1109/CommNet52204.2021.9641915.
N. Jaiswal, A. Pandey, S. Yadav, N. Purohit, D. S. Gurjar, “Physical layer security performance of NOMA-aided vehicular communications over Nakagami-m time-selective fading channels with channel estimation errors,” IEEE Open J. Veh. Technol., vol. 4, pp. 72–100, 2023, doi: https://doi.org/10.1109/OJVT.2022.3222187.
Y. M. Khattabi, S. A. Alkhawaldeh, M. M. Matalgah, O. S. Badarneh, R. Mesleh, “Vehicle-to-roadside-unit-to-vehicle communication system under different amplify-and-forward relaying schemes,” Veh. Commun., vol. 38, p. 100539, 2022, doi: https://doi.org/10.1016/j.vehcom.2022.100539.
D.-T. Do, T. Anh Le, T. N. Nguyen, X. Li, K. M. Rabie, “Joint impacts of imperfect CSI and imperfect SIC in cognitive radio-assisted NOMA-V2X communications,” IEEE Access, vol. 8, pp. 128629–128645, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3008788.
M. R. Mahmood, M. A. Matin, P. Sarigiannidis, S. K. Goudos, G. K. Karagiannidis, “Residual compensation-based extreme learning machine for MIMO-NOMA receiver,” IEEE Access, vol. 11, pp. 13398–13407, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3242917.
A. Emir, F. Kara, H. Kaya, X. Li, “Deep learning-based flexible joint channel estimation and signal detection of multi-user OFDM-NOMA,” Phys. Commun., vol. 48, p. 101443, 2021, doi: https://doi.org/10.1016/j.phycom.2021.101443.
B. A. O, J. Surendran, “Joint channel estimation and signal detection in MIMO-NOMA wireless systems using deep learning,” in 2023 IEEE International Conference on Recent Advances in Systems Science and Engineering (RASSE), 2023, pp. 1–6, doi: https://doi.org/10.1109/RASSE60029.2023.10363503.
R. Shankar, B. K. Sarojini, H. Mehraj, A. S. Kumar, R. Neware, A. Singh Bist, “Impact of the learning rate and batch size on NOMA system using LSTM-based deep neural network,” J. Def. Model. Simul. Appl. Methodol. Technol., vol. 20, no. 2, pp. 259–268, 2023, doi: https://doi.org/10.1177/15485129211049782.
R. Shankar, M. K. Beuria, S. S. Singh, F. Ana, H. Mehraj, V. G. Krishnan, “5G NOMA defense application environment and stacked LSTM network architectures,” J. Mob. Multimed., 2022, doi: https://doi.org/10.13052/jmm1550-4646.1914.
C. Nguyen, T. M. Hoang, A. A. Cheema, “Channel estimation using CNN-LSTM in RIS-NOMA assisted 6G network,” IEEE Trans. Mach. Learn. Commun. Netw., vol. 1, pp. 43–60, 2023, doi: https://doi.org/10.1109/TMLCN.2023.3278232.
H. Huang, Y. Yang, Z. Ding, H. Wang, H. Sari, F. Adachi, “Deep learning-based sum data rate and energy efficiency optimization for MIMO-NOMA systems,” IEEE Trans. Wirel. Commun., vol. 19, no. 8, pp. 5373–5388, 2020, doi: https://doi.org/10.1109/TWC.2020.2992786.
X. Wang, P. Zhu, D. Li, Y. Xu, X. You, “Pilot-assisted SIMO-NOMA signal detection with learnable successive interference cancellation,” IEEE Commun. Lett., vol. 25, no. 7, pp. 2385–2389, 2021, doi: https://doi.org/10.1109/LCOMM.2021.3070705.
A. Emir, F. Kara, H. Kaya, H. Yanikomeroglu, “Deep learning empowered semi-blind joint detection in cooperative NOMA,” IEEE Access, vol. 9, pp. 61832–61852, 2021, doi: https://doi.org/10.1109/ACCESS.2021.3074350.
H. Ye, G. Y. Li, B.-H. Juang, “Power of deep learning for channel estimation and signal detection in OFDM systems,” IEEE Wirel. Commun. Lett., vol. 7, no. 1, pp. 114–117, 2018, doi: https://doi.org/10.1109/LWC.2017.2757490.
D. K. Patel et al., “Performance analysis of NOMA in vehicular communications over i.n.i.d Nakagami-m fading channels,” IEEE Trans. Wirel. Commun., vol. 20, no. 10, pp. 6254–6268, 2021, doi: https://doi.org/10.1109/TWC.2021.3073050.
M. Gaballa, M. Abbod, A. Aldallal, “Investigating the combination of deep learning for channel estimation and power optimization in a non-orthogonal multiple access system,” Sensors, vol. 22, no. 10, p. 3666, 2022, doi: https://doi.org/10.3390/s22103666.
M. H. Rahman, M. A. S. Sejan, M. A. Aziz, Y.-H. You, H.-K. Song, “HyDNN: A hybrid deep learning framework based multiuser uplink channel estimation and signal detection for NOMA-OFDM system,” IEEE Access, vol. 11, pp. 66742–66755, 2023, doi: https://doi.org/10.1109/ACCESS.2023.3290217.
Z. Tang, J. Wang, J. Wang, J. Song, “On the achievable rate region of NOMA under outage probability constraints,” IEEE Commun. Lett., vol. 23, no. 2, pp. 370–373, 2019, doi: https://doi.org/10.1109/LCOMM.2018.2870584.
S. Li, M. Derakhshani, S. Lambotharan, “Outage-constrained robust power allocation for downlink MC-NOMA with imperfect SIC,” in 2018 IEEE International Conference on Communications (ICC), 2018, pp. 1–7, doi: https://doi.org/10.1109/ICC.2018.8422364.
M. Gaballa, M. Abbod, A. Jameel, “Power optimization analysis using throughput maximization in MISO non-orthogonal multiple access system,” in 2021 IEEE Globecom Workshops (GC Wkshps), 2021, pp. 1–6, doi: https://doi.org/10.1109/GCWkshps52748.2021.9682080.
M. Gaballa, M. Abbod, A. Jameel, N. Khaled, “Throughput maximization & power optimization analysis in non-orthogonal multiple access system,” in 2021 IEEE 4th 5G World Forum (5GWF), 2021, pp. 82–87, doi: https://doi.org/10.1109/5GWF52925.2021.00022.
Z. Yang, W. Xu, C. Pan, Y. Pan, M. Chen, “On the optimality of power allocation for NOMA downlinks with individual QoS constraints,” IEEE Commun. Lett., vol. 21, no. 7, pp. 1649–1652, 2017, doi: https://doi.org/10.1109/LCOMM.2017.2689763.
S. Boyd, L. Vandenberghe, Convex Optimization. Cambridge, UK: Cambridge University Press, 2004.
M. Gaballa, M. Abbod, M. Albasman, “Power allocation & MRC analysis for single input multi output non-orthogonal multiple access system,” in 2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), 2021, pp. 168–173, doi: https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics53846.2021.00038.
M. Chen, R. Furness, R. Gupta, S. Puchala, W. (Grace) Guo, “Hierarchical RNN-based framework for throughput prediction in automotive production systems,” Int. J. Prod. Res., vol. 62, no. 5, pp. 1699–1714, 2024, doi: https://doi.org/10.1080/00207543.2023.2199438.
S. Ghimire, R. C. Deo, H. Wang, M. S. Al-Musaylh, D. Casillas-Pérez, S. Salcedo-Sanz, “Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and new modeling results,” Energies, vol. 15, no. 3, p. 1061, 2022, doi: https://doi.org/10.3390/en15031061.
J. Zhang, Y. Jiang, S. Wu, X. Li, H. Luo, S. Yin, “Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism,” Reliab. Eng. Syst. Saf., vol. 221, p. 108297, 2022, doi: https://doi.org/10.1016/j.ress.2021.108297.
Z. Qin, H. Ye, G. Y. Li, B.-H. F. Juang, “Deep learning in physical layer communications,” IEEE Wirel. Commun., vol. 26, no. 2, pp. 93–99, 2019, doi: https://doi.org/10.1109/MWC.2019.1800601.
Y. Wang, R. Zhang, L. Yan, X. Ma, “Pilot chirp-assisted OCDM communications over time-varying channels,” IEEE Wirel. Commun. Lett., vol. 12, no. 9, pp. 1578–1582, 2023, doi: https://doi.org/10.1109/LWC.2023.3283672.
A. S. M. Mohammed, A. I. A. Taman, A. M. Hassan, A. Zekry, “Deep learning channel estimation for OFDM 5G systems with different channel models,” Wirel. Pers. Commun., vol. 128, no. 4, pp. 2891–2912, 2023, doi: https://doi.org/10.1007/s11277-022-10077-6.
A. Sherstinsky, “Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network,” Phys. D Nonlinear Phenom., vol. 404, p. 132306, 2020, doi: https://doi.org/10.1016/j.physd.2019.132306.
H. Kim, Y. Jiang, S. Kannan, S. Oh, P. Viswanath, “Deepcode: Feedback codes via deep learning,” IEEE J. Sel. Areas Inf. Theory, vol. 1, no. 1, pp. 194–206, 2020, doi: https://doi.org/10.1109/JSAIT.2020.2986752.
M. B. Mashhadi, D. Gunduz, A. Perotti, B. M. Popovic, “DRF codes: Deep SNR-robust feedback codes,” ITU J. Futur. Evol. Technol., vol. 4, no. 3, pp. 447–460, 2023, doi: https://doi.org/10.52953/DAPE6014.
Z. Ding, Z. Yang, P. Fan, H. V. Poor, “On the performance of non-orthogonal multiple access in 5G systems with randomly deployed users,” IEEE Signal Process. Lett., vol. 21, no. 12, pp. 1501–1505, 2014, doi: https://doi.org/10.1109/LSP.2014.2343971.
A. Simonetto, E. Dall’Anese, S. Paternain, G. Leus, G. B. Giannakis, “Time-varying convex optimization: Time-structured algorithms and applications,” Proc. IEEE, vol. 108, no. 11, pp. 2032–2048, 2020, doi: https://doi.org/10.1109/JPROC.2020.3003156.
L.-T. Tu et al., “Performance analysis of multihop full-duplex NOMA systems with imperfect interference cancellation and near-field path-loss,” Sensors, vol. 23, no. 1, p. 524, 2023, doi: https://doi.org/10.3390/s23010524.