Deep Learning TCP for Mitigating NLoS Impairments in 5G mmWave
DOI:
https://doi.org/10.15837/ijccc.2023.4.4874Keywords:
Deep learning, 5G, millimeter-wave, TCPAbstract
5G and beyond 5G are revolutionizing cellular and ubiquitous networks with new features and capabilities. The new millimeter-wave frequency band can provide high data rates for the new generations of mobile networks but suffers from NLoS caused by obstacles, which causes packet drops that mislead TCP because the protocol interprets all drops as an indication of network congestion. The principal flaw of TCP in such networks is that the root for packet drops is not distinguishable for TCP, and the protocol takes it for granted that all losses are due to congestion. This paper presents a new TCP based on deep learning that can outperform other common TCPs in terms of throughput, RTT, and congestion window fluctuation. The primary contribution of deep learning is providing the ability to distinguish various conditions in the network. The simulation results revealed that the proposed protocol could outperform conventional TCPs such as Cubic, NewReno, Highspeed, and BBR.References
Postel, J (1981); Transmission Control Protocol RFC 793, Updated by: RFC 1122, RFC 3168, RFC 6093, RFC 6528 [Online]. Available: https://tools.ietf.org/html/rfc793
Poorzare, R; Calveras Augé, A (2020); Challenges on the Way of Implementing TCP Over 5G Networks, IEEE Access, 8, 176393 - 176415, 2020.
https://doi.org/10.1109/ACCESS.2020.3026540
Zhang, M; et al (2019); Will TCP Work in mmWave 5G Cellular Networks?, IEEE Communications Magazine, 57(1), 65-71, 2019.
https://doi.org/10.1109/MCOM.2018.1701370
Poorzare, R; Calveras Augé, A (2021); FB-TCP: A 5G mmWave Friendly TCP for Urban Deployments, IEEE Access, 9, 82812-82832, 2021.
https://doi.org/10.1109/ACCESS.2021.3087239
Floyd, S (2003); HighSpeed TCP for Large Congestion Windows RFC 3649, [Online]. https://tools.ietf.org/html/rfc3649
https://doi.org/10.17487/rfc3649
Ha, S; Rhee, I; Xu, L (2008); CUBIC: a new TCP-friendly high-speed TCP variant, SIGOPS Operating Systems Review, 42(5), 64-74, 2008.
https://doi.org/10.1145/1400097.1400105
Cardwell, N; Cheng, Y; Gunn, C. S; Yeganeh, S. H; Jacobson, V (2016); BBR: Congestion-Based Congestion Control, Queue, 14(5), 20-53, 2016.
https://doi.org/10.1145/3012426.3022184
Henderson, T; Floyd, S; Gurtov, A; Nishida, Y (2012); The NewReno Modification to TCP's Fast Recovery Algorithm, RFC 6582, [Online]. https://tools.ietf.org/html/rfc6582.
https://doi.org/10.17487/rfc6582
Hindawi, B; Abbas, A. S (2021); Congestion Control Techniques in 5G mm Wave Networks: A review, 2021 1st Babylon International Conference on Information Technology and Science (BICITS), Babil, Iraq, 305-310, 2021.
https://doi.org/10.1109/BICITS51482.2021.9509879
Poorzare, R; Calveras Augé, A (2021); How Sufficient is TCP When Deployed in 5G mmWave Networks Over the Urban Deployment?, IEEE Access, 9, 36342-36355, 2021.
https://doi.org/10.1109/ACCESS.2021.3063623
Na, W; Bae, B; Cho, s; Kim, N (2019); DL-TCP: Deep Learning-Based Transmission Control Protocol for Disaster 5G mmWave Networks, IEEE Access, 7, 145134-145144, 2019.
https://doi.org/10.1109/ACCESS.2019.2945582
Kuppusamy, S.P; Subramaniam, M; Gunasekar, T (2023); Deep learning-based TCP congestion control algorithm for disaster 5G environment, Preprint, 2023.
https://doi.org/10.21203/rs.3.rs-2446108/v1
Najm, I.A; Hamoud, A.K; Lloret, J. S; Bosch, I (2019); Machine learning prediction approach to enhance congestion control in 5G IoT environment, Electronics, 8(6), 607, 2019.
https://doi.org/10.3390/electronics8060607
Kanagarathinam, M.R; et al. (2020); NexGen D-TCP: Next Generation Dynamic TCP Congestion Control Algorithm, IEEE Access, 8, 164482-164496, 2020.
https://doi.org/10.1109/ACCESS.2020.3022284
Diez, L; Fernández, A; Khan, M; Zaki, Y; Agüero, R (2020); Can We Exploit Machine Learning to Predict Congestion over mmWave 5G Channels?, Applied Sciences, 10(18), 6164, 2020.
https://doi.org/10.3390/app10186164
Khan, S; Hussain, A; Nazir, S; Khan, F; Oad, A; Alshehri, M.D (2022); Efficient and reliable hybrid deep learning-enabled model for congestion control in 5G/6G networks, Computer Communications, 182, 31-40, 2022.
https://doi.org/10.1016/j.comcom.2021.11.001
Lee, C; Cho, H; Song, S; Chung, J-M (2020); Prediction-Based Conditional Handover for 5G mm-Wave Networks: A Deep-Learning Approach, IEEE Vehicular Technology Magazine, 15(1), 54-62, 2020.
https://doi.org/10.1109/MVT.2019.2959065
Ford, A; Raiciu, C; Handley, M; Bonaventure, O; Paasch,C (2020); TCP Extensions for Multipath Operation with Multiple Addresse RFC 8684, [Online]. https://datatracker.ietf.org/doc/html/rfc8684.
https://doi.org/10.17487/RFC8684
Polese, P; Jana, R; Zorzi, M (2017); TCP and MP-TCP in 5G mmWave Networks, AIEEE Internet Computing, 21(5), 12-19, 2017.
https://doi.org/10.1109/MIC.2017.3481348
Polese, P; Jana, R; Zorzi, M (2017); TCP in 5G mmWave networks: Link level retransmissions and MP-TCP, in Proc. IEEE Conf. Comput. Com- mun. Workshops (INFOCOM WKSHPS), Atlanta, GA, USA, 343-348, 2017.
https://doi.org/10.1109/INFCOMW.2017.8116400
Poorzare, R; Waldhorst, O.P (2023); Toward the Implementation of MPTCP Over mmWave 5G and Beyond: Analysis, Challenges, and Solutions, IEEE Access, 11, 19534-19566, 2023.
https://doi.org/10.1109/ACCESS.2023.3248953
Mahmud, I; Lubna, T; Cho, Y-Z (2022); Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks, Sensors, 22(19), 856-858, 2022.
https://doi.org/10.3390/s22197509
Schmidhuber, J (2015); Deep learning in neural networks: An overview, Neural Networks, 61, 85-117, 2015
https://doi.org/10.1016/j.neunet.2014.09.003
Srivastava, I; Hinton, G; Krizhevsky, A; Sutskever, I; Salakhutdinov, R (2014); Dropout: A simple way to prevent neural networks from overfitting, Journal of Machine Learning Research, 15(1), 1929-1958, 2014.
Glorot, X; Bengio, Y (2010); Understanding the difficulty of training deep feedforward neural networks, in Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 9, 249-256, 2010.
Kingma, D.P; Ba, J (2014); UAdam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), 1412-6980, 2014.
Google; Google. TensorFlow, Accessed: May. 2021. [Online]. Available: https://www.tensorflow.org
Poorzare, R; DB-TCP's Source Codes, Accessed: Apr. 2021. [Online]. Available: https://github.com/rezapoorzare1/DB-TCP/tree/main.
Mezzavilla, M; et al. (2018); End-to-End Simulation of 5G mmWave Networks, IEEE Communications Surveys and Tutorials, 20(3), 2237-2263, 2018.
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