5G NR Uplink Performance Optimization: A Comprehensive Study on PRACH and PUSCH Interference Management

Désiré Guel; P. Justin Kouraogo; Flavien Hervé Somda; Boureima Zerbo; Oumarou Sié

doi:10.4108/eetmca.6933

Authors

Désiré Guel Université Joseph Ki-Zerbo
P. Justin Kouraogo Université Joseph Ki-Zerbo
Flavien Hervé Somda Université Joseph Ki-Zerbo
Boureima Zerbo Université Thomas SANKARA
Oumarou Sié Université Aube Nouvelle

DOI:

https://doi.org/10.4108/eetmca.6933

Keywords:

5G-NR, Physical Random Access Channel, PRACH, PUSCH, Intra/Inter-Cell Interference, Quality of Service, QoS, Block Error Rate, BLER

Abstract

The evolution of 5G New Radio (NR) technology offers unprecedented speeds, ultra-low latency, and the capability to connect billions of devices. However, these advancements come with significant challenges, particularly in managing interference during uplink communication. This study presents a comprehensive investigation into the optimization of 5G NR uplink performance by focusing on two critical channels: the Physical Uplink Shared Channel (PUSCH) and the Physical Random Access Channel (PRACH). The research explores the impact of intra-cell and inter-cell interference on these channels, highlighting how various User Equipment (UE) and cell configuration parameters influence performance. Key Performance Indicators (KPIs) such as Block Error Rate (BLER) and Correct Detection Rate (CDR) are utilized to assess the effectiveness of proposed interference management strategies. Through rigorous simulations and empirical evaluations, the study provides valuable insights into optimizing 5G NR networks, aiming to enhance the robustness and reliability of uplink communication in diverse interference scenarios. The findings underscore the importance of adaptive resource allocation and interference mitigation techniques in achieving superior network performance and quality of service (QoS).

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References

[1] E. Dahlman, S. Parkvall, and J. Skold. 5G NR: The nextgeneration wireless access technology. Academic Press, 2020.

[2] B. Bertenyi, S. Nagata, H. Kooropaty, X. Zhou,W. Chen, Y. Kim, X. Dai, and X. Xu. “5G NR radio interface”. In: Journal of ICT Standardization 6.1-2 (2018), pages 31–58. doi: 10.13052/jicts2245-800x.613.

[3] S. Parkvall, Y.W. Blankenship, R. Blasco, E. Dahlman, G. Fodor, S. J. Grant, E. Stare, and M. Stattin. “5G NR Release 16: Start of the 5G Evolution”. In: IEEE Commun. Stand. Mag. 4.4 (2020), pages 56–63. doi: 10.1109/MCOMSTD.011.1900018. url: https://ieeexplore.ieee.org/document/9316432.

[4] S. Ahmadi. 5G Network Architecture. 2019. doi: 10.1016/b978-0-08-102267-2.00001-4. url: https://linkinghub.elsevier.com/retrieve/pii/B9780081022672000014.

[5] A. Mukherjee. 5G New Radio: Beyond Mobile Broadband. Artech House, 2019. isbn: 9781630816421. url: https://www.artechhouse.com/Products/5G-New-Radio-Beyond-Mobile-Broadband-3758.aspx.

[6] B. Ozpoyraz, A. T. Dogukan, Y. Gevez, U. Altun, and E. BaÅ§ar. “Deep Learning-Aided 6G Wireless Networks: A Comprehensive Survey of Revolutionary PHY Architectures”. In: IEEE Open Journal of the Communications Society 3 (2022), pages 1749–1809. doi: 10.1109/ojcoms.2022.3210648. url: https://ieeexplore.ieee.org/document/9905727.

[7] E. C. V. Boas, J. D. S. e Silva, F. A. D. de Figueiredo, L. Mendes, and R. D. de Souza. “Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey”. In: EURASIP Journal on Wireless Communications and Networking 2022 (2022), pages 1–63. doi: 10.1186/s13638-022-02195-3. url: https://doi.org/10.1186/s13638-022-02195-3.

[8] M. Shammaa, M. Mashaly, and A. El-Mahdy. “A deep learning based adaptive receiver for full-duplex systems”. In: AEU -International Journal of Electronics and Communications (2023),. doi: 10.1016/j.aeue.2023.154822. url: https://www.sciencedirect.com/science/article/abs/pii/S1434841123002960?via=ihub.

[9] A. S. Doshi, M. Gupta, and J. Andrews. “Over-the-Air Design of GAN Training formm Wave MIMO Channel Estimation”. In: IEEE Journal on Selected Areas in Information Theory 3 (2022), pages 557–573. doi: 10.1109/sait.2022.3222479. url: https://ieeexplore.ieee.org/document/9953094.

[10] H. dos Santos Sousa, J. A. Soares, K. S. Mayer, and D. Arantes. “CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix”. In: ArXiv abs/2308.13623 (2023), null. doi: 10.14209/sbrt.2023.1570923809. url: https://arxiv.org/abs/2308.13623.

[11] S.-W. Jeon and C. Suh. “Degrees of freedom of uplinkdownlink multiantenna cellular networks”. In: 2014 IEEE International Symposium on Information Theory (2014), pages 1593–1597. doi: 10.1109/isit.2014.6875102. url: https://ieeexplore.ieee.org/document/6875102.

[12] A. B. Dayi. “Improving 5g nr uplink channel estimation with artificial neural networks: a practical study on NR PUSCH receiver”. In: 2022 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). IEEE. 2022, pages 129–134. doi: 10.1109/blackseacom54372.2022.9858290. url: https://ieeexplore.ieee.org/document/9858290.

[13] R. Fang, H. Chen, and W. Liu. “Deep Learning-Based PRACH Detection Algorithm Design and Simulation”. In: 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies (ICFEICT). 2022, pages 505–511. doi: 10.1109/ICFEICT57213.2022.00094. url: https://ieeexplore.ieee.org/document/10009473.

[14] G. Schreiber and M. Tavares. “5G New Radio Physical Random Access Preamble Design”. In: 2018 IEEE 5G World Forum (5GWF). 2018, pages 215–220. doi: 10.1109/5GWF.2018.8517052. url: https://ieeexplore.ieee.org/document/8517052.

[15] Q. Xiong, B. Yu, C. Qian, X. Li, and C. Sun. “Random Access Preamble Generation and Procedure Design for 5GNR System”. In: 2018 IEEE Globecom Workshops (GC Wkshps). 2018, pages 1–5. doi: 10.1109/glocomw.2018.8644175. url: https://ieeexplore.ieee.org/document/8644175.

[16] P. Gu, Y. Song, and C. Liu. “Channel Estimation of mmWave Massive MIMO System Based on Manifold Learning”. In: 2022 IEEE 22nd International Conference on Communication Technology (ICCT) null (2022), pages 1004–1008. doi: 10.1109/icct56141.2022.10073197. url: https://ieeexplore.ieee.org/document/10073197.

[17] Y. Li, X. Bian, and M. Li. “Denoising Generalization Performance of Channel Estimation in Multipath Time-Varying OFDM Systems”. In: Sensors (Basel, Switzerland) 23 (2023),. doi: 10 . 3390/s23063102. url: https://www.mdpi.com/1424-8220/23/6/3102.

[18] Y. Sun, Y. Cheng, T. Liu, Q. Huang, J. Guo, and W. Jin. “Research on Signal Detection of OFDM Systems Based on the LSTM Network Optimized by the Improved Chameleon Swarm Algorithm”. In: Mathematics (2023),. doi: 10.3390/math11091989. url: https://www.mdpi.com/2227-7390/11/9/1989.

[19] S. Haq, A. K. Gizzini, S. Shrey, S. Darak, S. Saurabh, and M. Chafii. “Deep Neural Network Augmented Wireless Channel Estimation for Preamble-Based OFDM PHY on Zynq System on Chip”. In: IEEE Transactions on Very Large Scale Integration (VLSI) Systems 31 (2022), pages 1026–1038. doi: 10.36227/techrxiv.20443917. url: https://ieeexplore.ieee.org/document/10129990.

[20] C. Song, X. Zhou, C. Wang, and Z. Ye. “A Double-Threshold Channel Estimation Method Based on Adaptive Frame Statistics”. In: Mathematics (2023),. doi: 10.20944/preprints202306.1247.v1. url: https://www.mdpi.com/2227-7390/11/15/3342.

[21] I. Shomorony and A. Avestimehr. “Multihop Wireless Networks: A Unified Approach to Relaying and Interference Management”. In: Found. Trends Netw. 8 (2014), pages 149–280. doi: 10.1561/1300000044. url: https://www.nowpublishers.com/article/Details/NET-044.

[22] Huawei. R1-071409: Efficient Matched Filters for Paired Root Zadoff-Chu Sequences. 3GPP TSG RAN WG1, meeting 48bis, St Julians, Malta. Available online at https://www.3gpp.org/ftp/tsg_ran/WG1_RL1/TSGR1_48bis/Docs/R1-071409.zip.2007.

[23] S. Sesia, I. Toufik, and M. Baker. LTE âĂŞ The UMTS Long Term Evolution: From Theory to Practice. John Wiley & Sons, 2011. isbn: 9780470660256. doi: 10.1002/9780470978504.url: https://onlinelibrary.wiley.com/doi/book/10.1002/9780470978504.

[24] Z. Shawqi and S. Y. Ameen. “Deep Learning Based Channel Estimation for 5G and Beyond”. In: IEEE Communications Surveys & Tutorials (2023). doi: 10.26682/csjuod.2023.26.2.46.

[25] W. Du, Z. Liu, and F. Li. “Interference Neutralization With Partial CSIT for Full-Duplex Cellular Networks”. In: IEEE Access 7 (2019), pages 49177–49190. doi: 10.1109/access.2019.291060. url: https://ieeexplore.ieee.org/document/8688408/.

[26] P. Aquilina. “Advanced interference management techniques for future generation cellular networks”. In: (2017). url: https://www.semanticscholar.org/paper/b20d3b0714e247f243a8d884108ebdc8b7a7cf54.

[27] M. Yang, S.-W. Jeon, and D. K. Kim. “On the Degrees of Freedom of Full-Duplex CellularNetworks”. In: ArXiv abs/1604.07957 (2016), url: https://www.semanticscholar.org/paper/6a318964ad3ec6be0ec629205d0822cbf1b3989d.

[28] G. Sridharan. “Interference Alignment in Cellular Networks”. Available online at TSpace, University of Toronto. PhD thesis. University of Toronto, 2015. url: https://utoronto.scholaris.ca/bitstreams/download.

[29] S. H. Chae and K. Lee. “Degrees of Freedom of Full-Duplex Cellular Networks: Effect of Self-Interference”. In: IEEE Transactions on Communications 65 (2017), pages 4507–4518. doi: 10.1109/tcomm.2017.2719022. url: https://ieeexplore.ieee.org/document/7956187.

[30] S.-W. Jeon, S. H. Chae, and S. Lim. “Degrees of freedom of full-duplex multiantenna cellular networks”. In: 2015 IEEE International Symposium on Information Theory (ISIT)(2015), pages 869–873. doi: 10.1109/isit.2015.7282579. url: https://ieeexplore.ieee.org/document/7282579.

[31] S.-W. Jeon and W.-Y. Shin. “Dynamic Opportunistic Interference Alignment for Random-Access Small-Cell Networks”. In: The Journal of Korean Institute of Communications and Information Sciences (2014), pages 675–681. doi: 10.7840/KICS.2014.39A.11.675. url: https://doi.org/10.7840/KICS.2014.39A.11.675.

[32] F. Launay. Les rÃľseaux de mobiles 4G et 5G: PRACH. Blog post. Accessed: August 12, 2024. 2022. url: https://blogs.univpoitiers.fr/f-launay/tag/prach/.

[33] Z. Guo, Y. Yuan, and Y. Chen. “5G NR Uplink Coverage Enhancement Based on DMRS Bundling and Multi-slot Transmission”. In: 2020 IEEE 20th International Conference on Communication Technology (ICCT). 2020, pages 482–486. doi: 10.109/ICCT50939.2020.9295747. url: https://ieeexplore.ieee.org/document/9295747/.

[34] S. S. Demirsoy and K. Marks. “SoC framework for FPGA: A case study of LTE PUSCH receiver”. In: 2009 IEEE International SOC Conference (SOCC). IEEE. 2009, pages 29–32. doi: 10.1109/soccon.2009.5398103. url: https://ieeexplore.ieee.org/document/5398103.

[35] D. H. Morais. 5G NR Overview. 2023. doi: 10.1007/978-3-031-33812-0_8. url: https://doi.org/10.1007/978- 3-031-33812-0_8.

[36] S. Kannan and P. Viswanath. “Capacity of Multiple Unicast in Wireless Networks: A Polymatroidal Approach”. In: IEEE Transactions on Information Theory 60 (2011), pages 6303–6328. doi: 10.1109/tit.2014.2347277. url: https://ieeexplore.ieee.org/document/6877730/.

[37] The MathWorks, Inc. MATLAB version: 9.13.0 (R2022b). Accessed: January 01, 2023. 2022. url: https://www.mathworks.com.