Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions

Authors

  • Sanjaya Kumar Sarangi Department of Computer Science, Utkal University, Bhubaneswar, India
  • Rasmita Lenka School of Electronics, KIIT Deemed to be University, Campus 12, Odisha, India
  • Ravi Shankar Department of Electronics and Communication Engineering, SR University, Warangal, India
  • Haider Mehraj Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, J & K, India
  • V. Gokula Krishnan Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai – 602105, Tamil Nadu, India

DOI:

https://doi.org/10.13052/jmm1550-4646.1948

Keywords:

DL, MMSE, LS, OFDM, Bi-LSTM, LSTM

Abstract

Fifth generation (5G) wireless networks’ system performance is dependent on having perfect knowledge of the channel state information (CSI). Deep learning (DL) has helped improve both the end-to-end reliability of 5G and beyond fifth generation (B5G) networks and the computational complexity of these networks. This work uses the Bi-linear long short-term memory (Bi-LSTM) scheme to examine the overall performance of the 5G orthogonal frequency division multiplexing (OFDM) technology. The least squares (LS) channel estimation scheme is a famous scheme employed to estimate the fading channel coefficients due to their lower complexity without the prior CSI. However, this scheme has an exceedingly high CSI error. Using pilot symbols (PS) and loss functions, this work has proposed the Bi-LSTM 5G OFDM estimators to improve the channel estimation obtained by the LS approach. All simulation analysis uses convex optimization (CO) software (CVX software) and stochastic gradient descent (SGD). When combined with many PS (72) and a cross-entropy loss function, the proposed Bi-LSTM outperforms the long-short-term memory (LSTM) cross-entropy, LS, and minimum mean square error (MMSE) estimators in low, medium, and high signal-to-noise ratio (SNR) regimes. The computational and training times of Bi-LSTM and LSTM DL estimators are also compared. Because of its DNN design, it can evaluate massive datasets, find hidden statistical patterns and characteristics, establish underlying relationships, and transfer what it has learnt to other contexts. Statistical analysis of the bit error rate (BER) reveals that Bi-LSTM outperforms the MMSE in terms of accurate channel prediction.

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Author Biographies

Sanjaya Kumar Sarangi, Department of Computer Science, Utkal University, Bhubaneswar, India

Sanjaya Kumar Sarangi, Senior Member IEEE and ACM. Dept. of Computer Science, Utkal University, distinguished background in Academic, Research and Industry sectors combined with 22 years of experiences for the knowledge towards achieving the institutions objectives though skill, knowledge and to nourish the global educational system. He holds PhD in Computer Science, Master of Technology in Comp.Sc & Engg. He was visiting Doctoral Fellow in the University of California, USA. His Research findings are in Wireless Ad hoc and Sensor Network, IDS, Mobile Communications, Geospatial Science and Remote Sensing, Climate Change and Disaster prediction system. He has number of publications in Journals, Patents and Conferences. His interest also in Information and Communication Technology (ICT) to enhance and optimize the information and worldwide research that can lead to be improved the student learning and teaching methods. Also undertakes the ICT based support for the smooth functioning of e-learning system for the universities.

Rasmita Lenka, School of Electronics, KIIT Deemed to be University, Campus 12, Odisha, India

Rasmita Lenka received the B.TECH degree in Electronics & Tele Comm. Engineering from Biju Pattnaik. University of Technology, in 2004, M.TECH degree in Communication System from Veer SurendraSai University of Technology (VSSUT) ODISHA in 2010. She completed her Ph.D in KIIT Deemed To Be University. She has 16 years of teaching experience and currently working as an Assistant Professor in School of Electronics KIIT Deemed University, Bhubaneswar. Her research area broadly includes signal processing and Image processing and its application. Her interest includes: Wireless sensor network, Mobile computing AI.

Ravi Shankar, Department of Electronics and Communication Engineering, SR University, Warangal, India

Ravi Shankar received his BE degree in Electronics and Communication Engineering from Jiwaji University, Gwalior, India, in 2006. He received his MTech degree in Electronics and Communication Engineering from GGSIPU, New Delhi, India, in 2012. He received a PhD in Wireless Communication from the National Institute of Technology Patna, Patna, India, in 2019. He was an assistant professor at MRCE Faridabad, from 2013 to 2014, where he was engaged in researching wireless communication networks. He is presently an assistant professor at SR University, Warangal, India. His current research interests cover cooperative communication, D2D communication, IoT/M2M networks and networks protocols. He is a student member of IEEE.

Haider Mehraj, Department of Electronics and Communication Engineering, Baba Ghulam Shah Badshah University, J & K, India

Haider Mehraj received his B. Tech in Electronics and Communication Engineering from the Guru Nanak Dev University, Amritsar, India in 2009, MTech in Communication and Information Technology from National Institute of Technology, Srinagar, India in 2011 and PhD in Biometrics at the National Institute of Technology, Srinagar, India in 2022. He is currently working as Assistant Professor in BGSB University, Rajouri, India. He has several national and international publications to his credit. His research interests include Biometrics, Image Processing, Deep Learning, and Pattern Recognition.

V. Gokula Krishnan, Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai – 602105, Tamil Nadu, India

V. Gokula Krishnan is currently working as Professor in the Department of Computer Science and Engineering in Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamil Nadu, India. He has completed his Under-Graduation (BE) in Anna University, Post-Graduation (M.Tech) in Dr. MGR University and Ph.D in Sathyabama Institute of Science and Technology, Chennai. He has more than 16 years of teaching experience in various colleges in Chennai and Hyderabad. He has published several papers in SCI/Scopus/WoS indexed journals and also he has presented various papers in National/International Conferences. His area of interest includes Computer Networks, Computer Architecture, Data Structures, Software Engineering etc. He serves as the guest editor, editorial member and also as reviewer in many reputed international journals. He is a member of Professional Bodies like ISTE, IAENG, CSI and IEEE etc

References

Pandya S, Wakchaure MA, Shankar R, Annam JR. “Analysis of NOMA-OFDM 5G wireless system using deep neural network”, The Journal of Defense Modeling and Simulation, vol. 19, Issue 4, pp. 799–806, 2022. DOI: 10.1177/1548512921999108.

R. Tiwari and S. Deshmukh, “Handover Count Based MAP Estimation of Velocity with Prior Distribution Approximated via NGSIM Data-Set,” in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 5, pp. 4352–4361, May 2022, DOI: 10.1109/TITS.2020.3043888.

Chaudhary BP, Shankar R, Mishra RK. “A tutorial on cooperative non-orthogonal multiple access networks”, The Journal of Defense Modeling and Simulation, vol. 19, Issue 4, pp. 563–573, 2022. DOI: 10.1177/1548512920986627.

Ravi Tiwari, Siddharth Deshmukh, “Analysis and design of an efficient handoff management strategy via velocity estimation in HetNets”, Transactions on Emerging Telecommunications Technologies, vol. 33, Issue 3, e3642, March 2022. https://doi.org/10.1002/ett.3642.

R. Tiwari and S. Deshmukh, “Prior Information-Based Bayesian MMSE Estimation of Velocity in HetNets,” in IEEE Wireless Communications Letters, vol. 8, no. 1, pp. 81–84, Feb. 2019. DOI: 10.1109/LWC.2018.2857805.

M. Agiwal, A. Roy and N. Saxena, “Next Generation 5G Wireless Networks: A Comprehensive Survey”, IEEE Communications Surveys & Tutorials, vol. 18, no. 3, pp. 1617–1655, 2016. DOI: 10.1109/COMST.2016.2532458.

Dankan Gowda V, Avinash Sharma, B. Kameswara Rao, Ravi Shankar, Parismita Sarma, Abhay Chaturvedi, Naziya Hussain, “Industrial quality healthcare services using Internet of Things and fog computing approach”, Measurement: Sensors, Elsevier, vol. 24, pp. 100517, 2022, ISSN 2665–9174, https://doi.org/10.1016/j.measen.2022.100517.K.

Shafique, B. A. Khawaja, F. Sabir, S. Qazi, and M. Mustaqim, “Internet of Things (IoT) for Next-Generation Smart Systems: A Review of Current Challenges, Future Trends and Prospects for Emerging 5G-IoT Scenarios,” in IEEE Access, vol. 8, pp. 23022–23040, 2020. DOI: 10.1109/ACCESS.2020.2970118.

X. Liu and X. Zhang, “Rate and Energy Efficiency Improvements for 5G-Based IoT with Simultaneous Transfer,” IEEE Internet of Things Journal, vol. 6, no. 4, pp. 5971–5980, Aug. 2019. DOI: 10.1109/JIOT.2018.2863267.

Ahmed, Rizwan and Malviya, Anil Kumar and Kaur, Maninder Jeet and Mishra, Ved P., “Comprehensive Survey of Key Technologies Enabling 5G-IoT”, Proceedings of 2nd International Conference on Advanced Computing and Software Engineering (ICACSE) 2019, Available at SSRN: https://ssrn.com/abstract=3351007~or~http://dx.doi.org/10.2139/ssrn.3351007.

M. H. Mahmud, K. Khaleduzzaman, S. Sarker and L. Chandra Paul, “Effect of Path Loss Models on Performance of 5G Compatible MIMO window-OFDM Systems”, International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), pp. 257–262, 2020. DOI: 10.1109/ICSTCEE49637.2020.9277121.

Bhardwaj, Lokesh, Ritesh Kumar Mishra, and Ravi Shankar. “Investigation of Low-Density Parity Check Codes Concatenated Multi-User Massive Multiple-Input Multiple-Output Systems with Imperfect Channel State Information.” The Journal of Defense Modeling and Simulation, vol. 19, no. 3, pp. 539–50, July 2022. https://doi.org/10.1177/1548512920968639.

T. Li, X. Hao, and X. Yue, “A Power Domain Multiplexing Based Co-Carrier Transmission Method in Hybrid Satellite Communication Networks,” in IEEE Access, vol. 8, pp. 120036–120043, 2020, DOI: 10.1109/ACCESS.2020.3005860.

Ravi Shankar, Ritesh Kumar Mishra, “S-DF Cooperative Communication System over Time Selective Fading Channel”, Journal of Information science and Engineering, vol. 35, Issue 6, pp. 1223–1248, 2019. DOI: 10.6688/JISE.201911_35(6).0004.

Das, A.K., Pramanik, A., “A Survey Report on Underwater Acoustic Channel Estimation of MIMO-OFDM System”, In: Bhattacharjee, D., Kole, D.K., Dey, N., Basu, S., Plewczynski, D. (eds) Proceedings of International Conference on Frontiers in Computing and Systems. Advances in Intelligent Systems and Computing, vol. 1255, 2021. Springer, Singapore. https://doi.org/10.1007/978--981-15--7834-2_69.

Chitikena, Rajeshbabu, and P. Estherrani. “Time-Varying Analysis of Channel Estimation in OFDM.” Advances in Smart System Technologies, pp. 669–678. Springer, Singapore, 2021.

N. Taherkhani and S. Pierre, “Centralized and Localized Data Congestion Control Strategy for Vehicular Ad Hoc Networks Using a Machine Learning Clustering Algorithm,” IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 11, pp. 3275–3285, Nov. 2016. DOI: 10.1109/TITS.2016.2546555.

J. Kwak, Y. Kim, L. B. Le, and S. Chong, “Hybrid Content Caching in 5G Wireless Networks: Cloud Versus Edge Caching,” IEEE Transactions on Wireless Communications, vol. 17, no. 5, pp. 3030–3045, May 2018, doi: 10.1109/TWC.2018.2805893.

Z. Chang, L. Lei, Z. Zhou, S. Mao, and T. Ristaniemi, “Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era,” IEEE Wireless Communications, vol. 25, no. 3, pp. 28–35, June 2018, doi: 10.1109/MWC.2018.1700317.

A. Saeed and M. Kolberg, “Towards Optimizing WLANs Power Saving: Novel Context-Aware Network Traffic Classification Based on a Machine Learning Approach,” IEEE Access, vol. 7, pp. 3122–3135, 2019, DOI: 10.1109/ACCESS.2018.2888813.

E. Bastug, M. Bennis, E. Zeydan, M. A. Kader, I. A. Karatepe, A. S. Er, and M. Debbah, “Big data meets telcos: A proactive caching perspective,” J. Commun. Netw., vol. 17, no. 6, pp. 549–557, Dec. 2015. https://doi.org/10.48550/arXiv.1602.06215.

A. Imran, A. Zoha, and A. Abu-Dayya, “Challenges in 5G: How to empower SON with big data for enabling 5G,” IEEE Netw., vol. 28, no. 6, pp. 27–33, Nov./Dec. 2014.

M. E. Morocho-Cayamcela, H. Lee and W. Lim, “Machine Learning for 5G/B5G Mobile and Wireless Communications: Potential, Limitations, and Future Directions,” in IEEE Access, vol. 7, pp. 137184–137206, 2019, doi: 10.1109/ACCESS.2019.2942390.

F. Kadri, M. Baraoui and I. Nouaouri, “An LSTM-based DL Approach with Application to Predicting Hospital Emergency Department Admissions,” 2019 International Conference on Industrial Engineering and Systems Management (IESM), pp. 1–6, 2019. DOI: 10.1109/IESM45758.2019.8948130.

Kaushik, S., Choudhury, A., Dasgupta, N., Natarajan, S., Pickett, L.A., Dutt, V., “Ensemble of Multi-headed Machine Learning Architectures for Time-Series Forecasting of Healthcare Expenditures”, In: Johri, P., Verma, J., Paul, S. (eds) Applications of Machine Learning. Algorithms for Intelligent Systems. Springer, Singapore, 2020. https://doi.org/10.1007/978--981-15--3357-0_14.

Batbaatar E, Ryu KH., “Ontology-Based Healthcare Named Entity Recognition from Twitter Messages Using a Recurrent NN Approach”, International Journal of Environmental Research and Public Health, vol. 16, pp. 3628, 2019.

Thien HT, Tuan PV, Koo I, “Deep Learning-Based Approach to Fast Power Allocation in SISO SWIPT Systems with a Power-Splitting Scheme”, Applied Sciences, vol. 10, pp. 3634, 2020.

L. Fernández Maimó, Á. L. Perales Gómez, F. J. García Clemente, M. Gil Pérez, and G. Martínez Pérez, “A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks,” in IEEE Access, vol. 6, pp. 7700–7712, 2018, doi: 10.1109/ACCESS.2018.2803446.

M. Schuster and K. K. Paliwal, “Bidirectional recurrent neural networks,” in IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2673–2681, Nov. 1997, doi: 10.1109/78.650093.

Narengerile. Deep learning-based signal detection in OFDM systems. (https://www.mathworks.com/matlabcentral/fileexchange/72321-deep-learning-based-signal-detection-in-ofdm-systems), MATLAB Central File Exchange. Retrieved September 26, 2020.

Le HA, Van Chien T, Nguyen TH, et al., “Machine Learning-Based 5G-and-Beyond Channel Estimation for MIMO-OFDM Communication Systems”, Sensors, vol. 21, pp. 4861, 2021.

C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li, “Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach,” in IEEE Transactions on Network Science and Engineering, vol. 7, no. 1, pp. 227–236, 1 Jan.–March 2020. DOI: 10.1109/TNSE.2018.2848960.

Y. Yang, F. Gao, X. Ma, and S. Zhang, “Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels,” in IEEE Access, vol. 7, pp. 36579–36589, 2019. DOI: 10.1109/ACCESS.2019.2901066.

Q. Bai, J. Wang, Y. Zhang, and J. Song, “Deep Learning-Based Channel Estimation Algorithm Over Time Selective Fading Channels,” in IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 1, pp. 125–134, March 2020. DOI: 10.1109/TCCN.2019.2943455.

D Venkata Ratnam, K Nageswara Rao. “Bi-LSTM based deep learning method for 5G signal detection and channel estimation”, AIMS Electronics and Electrical Engineering, vol. 5, Issue 4, pp. 2021, 334–341, 2021. DOI: 10.3934/electreng.2021017.

Essai Ali MH, Taha IBM., “Channel state information estimation for 5G wireless communication systems: recurrent NNs approach”, PeerJ Computer Science, vol. 7, pp. e682, 2021. https://doi.org/10.7717/peerj-cs.682.

H. Ye, G. Y. Li and B. -H. Juang, “Power of Deep Learning for Channel Estimation and Signal Detection in OFDM Systems,” in IEEE Wireless Communications Letters, vol. 7, no. 1, pp. 114–117, Feb. 2018. DOI: 10.1109/LWC.2017.2757490.

J. M. Kang, C. J. Chun, and I. M. Kim, “Deep Learning Based Channel Estimation for MIMO Systems with Received SNR Feedback,” in IEEE Access, vol. 8, pp. 121162–121181, 2020. DOI: 10.1109/ACCESS.2020.3006518.

Ponnaluru, S., Penke, S. “Deep learning for estimating the channel in orthogonal frequency division multiplexing systems”, J Ambient Intell Human Comput, vol. 12, pp. 5325–5336, 2021. https://doi.org/10.1007/s12652-020-02010-1.

Essai Ali MH., “Deep learning-based pilot-assisted channel state estimator for OFDM systems”, IET Communications, vol. 15, Issue 2, pp. 257–264, 2021. https://doi.org/10.1049/cmu2.12051.

A. Sarwar, S. M. Shah, and I. Zafar, “Channel Estimation in Space Time Block Coded MIMO-OFDM System using Genetically Evolved Artificial Neural Network,” 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 703–709, 2020. DOI: 10.1109/IBCAST47879.2020.9044539.

Senol, H., Bin Tahir, A.R. and Özmen, A., “Artificial neural network-based estimation of sparse multipath channels in OFDM systems”, Telecommun Syst, vol. 77, pp. 231–240, 2021. https://doi.org/10.1007/s11235--021-00754--5.

A. L. Ha, T. Van Chien, T. H. Nguyen, W. Choi, and V. D. Nguyen, “Deep Learning-Aided 5G Channel Estimation,” 15th International Conference on Ubiquitous Information Management and Communication (IMCOM), 2021, pp. 1–7. DOI: 10.1109/IMCOM51814.2021.9377351.

H. Deng and B. Himed, “Interference Mitigation Processing for Spectrum-Sharing Between Radar and Wireless Communications Systems,” in IEEE Transactions on Aerospace and Electronic Systems, vol. 49, no. 3, pp. 1911–1919, July 2013. DOI: 10.1109/TAES.2013.6558027.

Janocha K, Czarnecki WM Japa., “On loss functions for deep NNs in classification”, arXiv preprint. arXiv:1702.05659, 2017.

Ketkar, Nikhil. “Stochastic gradient descent.” In Deep learning with Python, Apress, Berkeley, CA, pp. 113–132, 2017.

Scaman, Kevin, Cedric Malherbe, and Ludovic Dos Santos. “Convergence Rates of Non-Convex Stochastic Gradient Descent Under a Generic Lojasiewicz Condition and Local Smoothness.” In International Conference on Machine Learning, pp. 19310–19327. PMLR, 2022.

Li, Qing, Diwen Xiong, and Mingsheng Shang. “Adjusted stochastic gradient descent for latent factor analysis.” Information Sciences, vol. 588, pp. 196–213, 2022.

Lou, Zhipeng, Wanrong Zhu, and Wei Biao Wu. “Beyond sub-gaussian noises: Sharp concentration analysis for stochastic gradient descent.” Journal of Machine Learning Research, vol. 23, pp. 1–22, 2022.

Bianchi, Pascal, Walid Hachem, and Sholom Schechtman. “Convergence of constant step stochastic gradient descent for non-smooth non-convex functions.” Set-Valued and Variational Analysis, pp. 1–31, 2022.

Backhoff-Veraguas, Julio, Joaquin Fontbona, Gonzalo Rios, and Felipe Tobar. “Stochastic gradient descent in Wasserstein space.” arXiv preprint arXiv:2201.04232, 2022.

Vijayalakshmi, Kaliyamoorthy, Krishnasamy Vijayakumar, and Kandasamy Nandhakumar. “Prediction of virtual energy storage capacity of the air-conditioner using a stochastic gradient descent based artificial neural network”. Electric Power Systems Research, vol. 208, pp. 107879, 2022.

Hua, Hang, Xiaoming Wang, and Youyun Xu. “Signal detection in uplink pilot-assisted multi-user MIMO systems with deep learning.” In Computing, Communications, and IoT Applications (ComComAp), IEEE, pp. 369–373, 2019.

Hochreiter S, Schmidhuber J., “Long short-term memory”, Neural Computation, vol. 9, pp. 1735–1780, 1997. DOI: 10.1162/neco.1997.9.8.1735.

Ravi Shankar, Manoj Kumar Beuria, Gopal Ramchandra Kulkarni, Abu Sarwar Zamani, Patteti Krishna, V. Gokula Krishnan, “Examination of the DL Based Ubiquitous Multiple Input Multiple Output U/L NOMA System Considering Robust Fading Channel Conditions for Military Communication Scenario”, Journal of Information Science and Engineering, vol. 38, Issue 6, 2022. DOI: 10.6688/JISE.20221138(6).0013.

Ravi Shankar, Ayaz Ahmad, Saminathan Veerappan, Manoj Kumar Beuria, Patteti Krishna, Sudhansu Sekhar Singh, V. Gokula Krishnan “Examination of User Pairing NOMA System Considering the DQN Scheme over Time-Varying Fading Channel Conditions”. Journal of Information Science and Engineering, vol. 38, pp. 859–875, July 2022. DOI: 10.6688/JISE.20220738(4).0010.

Kumar, Indrajeet, Vikash Sachan, Ravi Shankar, and Ritesh Kumar Mishra. “An investigation of wireless S-DF hybrid satellite terrestrial relaying network over time selective fading channel.” Traitement du Signal, vol. 35, no. 2, pp. 103, 2018.

Shankar, Ravi, Indrajeet Kumar, and Ritesh Kumar Mishra. “Pairwise Error Probability Analysis of Dual Hop Relaying Network over Time Selective Nakagami-m Fading Channel with Imperfect CSI and Node Mobility.” Traitement du Signal, vol. 36, no. 3, 2019.

A. Kumar, D. Chaturvedi, M. Saravanakumar, and S. Raghavan, “SIW Cavity-Backed Self-Triplexing Antenna with T-Shaped Slot,” Asia-Pacific Microwave Conference (APMC), 2018, pp. 1588–1590, doi: 10.23919/APMC.2018.8617526.

D. Chaturvedi, A. Kumar, and S. Raghavan, “Wideband HMSIW-based slotted antenna for wireless fidelity application,” IET Microw. Antennas Propag., vol. 13, no. 2, pp. 258–262, 2019.

A. Kumar and S. Raghavan, “A design of miniaturized half-mode SIW cavity backed antenna,” 2016 IEEE Indian Antenna Week (IAW 2016), pp. 4–7, 2016. DOI: 10.1109/IndianAW.2016.7883585.

A. Kumar and S. Raghavan, “Design of SIW cavity-backed self-triplexing antenna,” Electron. Lett., vol. 54, no. 10, pp. 611–612, 2018.

A. Kumar and M. J. Al- Hasan, “A coplanar-waveguide-fed planar integrated cavity backed slotted antenna array using TE 33 mode,” Int. J. RF Microw. Comput-Aid. Eng., vol. 30, no. 10, 2020.

A. Kumar and S. Raghavan, “Broadband dual-circularly polarised SIW cavity antenna using a stacked structure,” Electron. Lett., vol. 53, no. 17, pp. 1171–1172, 2017.

Al-Saggaf, Ubaid M., Muhammad Moinuddin, Syed Saad Azhar Ali, Syed Sajjad Hussain Rizvi, and Muhammad Faisal. “Machine Learning Aided Channel Equalization in Filter Bank Multi-Carrier Communications for 5G.” Wearable and Neuronic Antennas for Medical and Wireless Applications (2022): 1–9.

Emil Björnson, Jakob Hoydis and Luca Sanguinetti, “Massive MIMO Networks: Spectral, Energy, and Hardware Efficiency”, Foundations and Trends in Signal Processing, vol. 11, Issue 3–4, pp. 154–655, 2017, http://dx.doi.org/10.1561/2000000093.

Van Chien, T., Björnson, E., Larsson, E.G. “Joint pilot design and uplink power allocation in multi-cell Massive MIMO systems”, IEEE Trans. Wirel. Commun., vol. 17, pp. 2000–2015, 2018.

Björnson, E., Hoydis, J., Sanguinetti, L., “Massive MIMO has unlimited capacity”, IEEE Trans. Wirel. Commun., vol. 17, pp. 574–590, 2018.

Van Chien, T., Ngo, H.Q., Chatzinotas, S., Ottersten, B., Debbah, M. “Uplink Power Control in Massive MIMO with Double Scattering Channels”, arXiv 2021, arXiv:2103.04129.

Wu, S., Wang, C.X., Haas, H., Alwakeel, M.M., Ai, B., “A non-stationary wideband channel model for massive MIMO communication systems”, IEEE Trans. Wirel. Commun., vol. 14, pp. 1434–1446, 2014.

J. Yuan, H. Q. Ngo, and M. Matthaiou, “Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging,” IEEE Transactions on Wireless Communications, vol. 19, no. 5, pp. 2960–2973, May 2020, doi: 10.1109/TWC.2020.2969627.

Dong, Peihao, Hua Zhang, Geoffrey Ye Li, Ivan Simoes Gaspar, and Navid NaderiAlizadeh. “Deep CNN-based channel estimation for mmWave massive MIMO systems.” IEEE Journal of Selected Topics in Signal Processing, vol. 13, no. 5, pp. 989–1000, 2019.

Kawakami, Kazuya. “Supervised sequence labelling with recurrent neural networks.” PhD diss., Technical University of Munich, 2008.

Seeliger, Alexander, Stefan Luettgen, Timo Nolle, and Max Mühlhäuser. “Learning of process representations using recurrent neural networks”, International Conference on Advanced Information Systems Engineering, pp. 109–124. Springer, Cham, 2021.

Kang, J.M., Chun, C.J., Kim, I.M., Kim, D.I. “Deep RNN-Based Channel Tracking for Wireless Energy Transfer System”, IEEE Syst. J., vol. 14, pp. 4340–4343, 2020.

Pan, Jing, Hangguan Shan, Rongpeng Li, Yingxiao Wu, Weihua Wu, and Tony QS Quek. “Channel estimation based on deep learning in vehicle-to-everything environments.” IEEE Communications Letters, vol. 25, no. 6, 1891–1895, 2021.

Manu Rastogi, Tutorial on LSTMs: A Computational Perspective, Apr 6, 2020. Online [https://towardsdatascience.com/tutorial-on-lstm-a-computational-perspective].

Study on Channel Model for Frequencies from 0.5 to 100 GHz (Release 15). Technical Report. 3GPP TR 38.901. 2018. Available online: https://www.3gpp.org/DynaReport/38901.htm (accessed on 14 July 2021)

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2023-05-04

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Sarangi, S. K. ., Lenka, R. ., Shankar, R. ., Mehraj, H. ., & Krishnan, V. G. . (2023). Examination of the Bi-LSTM Based 5G-OFDM Wireless Network Over Rayleigh Fading Channel Conditions. Journal of Mobile Multimedia, 19(04), 1067–1106. https://doi.org/10.13052/jmm1550-4646.1948

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2023: Vol 19 Iss 4

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