Meta surface Assisted Open radio access networks

Authors

  • Haneen A.Ajeel college of engineering
  • Ismail Hburi Electrical Engineering Department, College of Engineering, Wasit University, Wasit, Iraq

DOI:

https://doi.org/10.31185/ejuow.Vol12.Iss4.550

Keywords:

Open-radio access network (O-RAN); Reconfigurable Intelligent Surfaces (RIS); beamforming; duality theory; user-equipment association; transform of quadratic function.

Abstract

The paradigm of the open radio access networks (O-RAN) seeks to carry intelligence and openness (multi-vendors) to the conventional proprietary and closed radio access networks (RAN) schemes and deliver performance enhancement, cost-efficiency, and flexibility, in both the network's operation and deployment. On the other hand, Reconfigurable Intelligent Surfaces (RIS) are suggested for future networks due to their effectiveness in terms of cost and energy consumption. However, due to the fast time-varying feature of the dense wireless systems, it becomes difficult to allow optimal user association and beamforming in terms of signalling overheads and processing in RIS assisted RANs with the limited capacity of the fronthaul. Therefore, the objective of this study is to attain a trade-off between the costs (signalling overhead/complexity) and the throughput performance. In other words, the study sets the challenges of the RIS aided O-RAN technology regarding the joint selection of user’s equipment (UEs)/open radio unit (O-RU)-RIS pairs and the designing of beamforming at the O-RU/RIS and open distributed unit (O-DU). From this point, to addressing the designing challenges, this work suggests a simple and potential beamforming strategy in RIS aided O-RAN architecture taking into account the specification of the interfaces between different O-RAN units to split opportunities between the radio and distributing units. In specific, a channel-gain-based selection of UEs/O-RU-RIS pairs joint with duality theory (DT) and transform of quadratic function (TQF) algorithm (namely DT_TQF) is proposed. Firstly, the non-convex optimization problem is relaxed via duality and transform of quadratic functions, and then an iterative approach is carried out for the active and passive beamformers via a simple alternating optimization approach. This approach can achieve flexibility in the environment of a high-traffic transmission while lowering the interference between radio units and the signalling burden required for beamforming tasks. Numerical simulation results justify the effectiveness of the algorithm for different systems' parameter settings and validate the important of installing RIS. For example, for a certain environment, the performance gain is about 52.9 % in comparison to the classic null-steering/random phase shifter scheme.

References

Jiang, W., et al., The road towards 6G: A comprehensive survey. 2021. 2: p. 334-366.

Hburi, I.S. and H.F. Khazaal. Joint RRH selection and power allocation forEnergy-efficient C-RAN systems. in 2018 Al-Mansour International Conference on New Trends in Computing, Communication, and Information Technology (NTCCIT). 2018. IEEE.

Han, C., Y. Wu, and Z.J.I.T.U. Chen, Network 2030 a blueprint of technology, applications and market drivers towards the year 2030 and beyond. 2018.

Hburi, I., et al. MISO-NOMA Enabled mm-Wave: Sustainable Energy Paradigm for Large Scale Antenna Systems. in 2021 International Conference on Advanced Computer Applications (ACA). 2021. IEEE.

Hua, M., et al., Intelligent reflecting surface-aided joint processing coordinated multipoint transmission. 2020. 69(3): p. 1650-1665.

Al-Shaeli, I., et al., Reconfigurable intelligent surface passive beamforming enhancement using unsupervised learning. 2023. 13(1): p. 493-501.

Hasan Fahad KHazaal, Ahmed Magdy, Iryna Svyd, IVAN OBOD, A Dumbbell Shape Reconfigurable Intelligent Surface for mm-wave 5G Application. International Journal of Intelligent Engineering and Systems, 2024. 17(6): p. 569-582.

Imoize, A.L., et al., A review of energy efficiency and power control schemes in ultra-dense cell-free massive MIMO systems for sustainable 6G wireless communication. 2022. 14(17): p. 11100.

Salim, H., et al., Reconfigurable Intelligent Surfaces Between the Reality and Imagination. Wasit Journal of Computer and Mathematics Science, 2024. 3(2): p. 42-50.

Polese, M., et al., Understanding O-RAN: Architecture, interfaces, algorithms, security, and research challenges. 2023. 25(2): p. 1376-1411.

Malandrino, F., et al. Performance and EMF exposure trade-offs in human-centric cell-free networks. in 2022 20th International Symposium on Modeling and Optimization in Mobile, Ad hoc, and Wireless Networks (WiOpt). 2022. IEEE.

Park, S.-H., et al., Fronthaul compression for cloud radio access networks: Signal processing advances inspired by network information theory. 2014. 31(6): p. 69-79.

Peng, M., et al., Fronthaul-constrained cloud radio access networks: Insights and challenges. 2015. 22(2): p. 152-160.

Kim, J., et al. Joint design of digital and analog processing for downlink C-RAN with large-scale antenna arrays. in 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC). 2017. IEEE.

Kang, J., et al., Layered downlink precoding for C-RAN systems with full dimensional MIMO. 2016. 66(3): p. 2170-2182.

Liu, L. and R.J.I.T.o.S.P. Zhang, Optimized uplink transmission in multi-antenna C-RAN with spatial compression and forward. 2015. 63(19): p. 5083-5095.

Liu, L., S. Bi, and R.J.I.T.o.C. Zhang, Joint power control and fronthaul rate allocation for throughput maximization in OFDMA-based cloud radio access network. 2015. 63(11): p. 4097-4110.

Agheli, P., M.J. Emadi, and H.J.a.p.a. Beyranvand, Designing cost-and energy-efficient cell-free massive MIMO network with fiber and FSO fronthaul links. 2020.

Wang, X., et al., Virtualized cloud radio access network for 5G transport. 2017. 55(9): p. 202-209.

Demir, Ö.T., et al., Cell-free massive MIMO in O-RAN: Energy-aware joint orchestration of cloud, fronthaul, and radio resources. 2024.

Pan, C., et al., Joint precoding and RRH selection for user-centric green MIMO C-RAN. 2017. 16(5): p. 2891-2906.

Ha, V.N. and L.B. Le. Computation capacity constrained joint transmission design for c-rans. in 2016 IEEE Wireless Communications and Networking Conference. 2016. IEEE.

Taha, A., M. Alrabeiah, and A.J.I.a. Alkhateeb, Enabling large intelligent surfaces with compressive sensing and deep learning. 2021. 9: p. 44304-44321.

Renzo, M.D., et al., Smart radio environments empowered by reconfigurable AI meta-surfaces: An idea whose time has come. 2019. 2019(1): p. 1-20.

Keti, F., et al., Spectral and energy efficiencies maximization in downlink NOMA systems. 2022. 11(3): p. 1449-1459.

Mishra, D.P., et al., Compact MIMO antenna using dual-band for fifth-generation mobile communication system. 2021. 24(2): p. 921-929.

Elbir, A.M., et al., Deep channel learning for large intelligent surfaces aided mm-wave massive MIMO systems. 2020. 9(9): p. 1447-1451.

Rashag, H.F. and M.H.J.I.J.o.A.i.A.S.I. Ali, Optimization of transmission signal by artificial intelligent. 2019. 2252(8814): p. 8814.

Huang, C., et al., Holographic MIMO surfaces for 6G wireless networks: Opportunities, challenges, and trends. 2020. 27(5): p. 118-125.

Dardari, D.J.I.J.o.S.A.i.C., Communicating with large intelligent surfaces: Fundamental limits and models. 2020. 38(11): p. 2526-2537.

Huang, C., et al., Reconfigurable intelligent surfaces for energy efficiency in wireless communication. 2019. 18(8): p. 4157-4170.

ElMossallamy, M.A., et al., Reconfigurable intelligent surfaces for wireless communications: Principles, challenges, and opportunities. 2020. 6(3): p. 990-1002.

Sokal, B., et al., Reducing the control overhead of intelligent reconfigurable surfaces via a tensor-based low-rank factorization approach. 2023. 22(10): p. 6578-6593.

Praia, J., et al., Phase shift optimization algorithm for achievable rate maximization in reconfigurable intelligent surface-assisted THz communications. 2021. 11(1): p. 18.

Alexandropoulos, G.C., et al. Phase configuration learning in wireless networks with multiple reconfigurable intelligent surfaces. in 2020 IEEE Globecom Workshops (GC Wkshps. 2020. IEEE.

Wang, P., et al., Intelligent reflecting surface-assisted millimeter wave communications: Joint active and passive precoding design. 2020. 69(12): p. 14960-14973.

Hburi, I., et al. Sub-array hybrid beamforming for sustainable largescale mmWave-MIMO communications. in 2021 International Conference on Advanced Computer Applications (ACA). 2021. IEEE.

HUbri, I., F.J.I.J.o.E.E. Hasan, and C. Science, An Efficient Two-Stage User Association Scheme for Green C-RAN Systems. 2019. 16(2): p. 793-802.

Baqer, I.S. A practical weighted sum rate maximisation for multi-stream cellular MIMO systems. in 2018 International Conference on Engineering Technology and their Applications (IICETA). 2018. IEEE.

Shen, K. and W.J.I.T.o.S.P. Yu, Fractional programming for communication systems—Part I: Power control and beamforming. 2018. 66(10): p. 2616-2630.

Singh, S.K., R. Singh, and B. Kumbhani. The evolution of radio access network towards open-RAN: Challenges and opportunities. in 2020 IEEE Wireless Communications and Networking Conference Workshops (WCNCW). 2020. IEEE.

Błaszczyszyn, B. and M.J.P.o.W. Karray, Paderborn, Linear-regression estimation of the propagation-loss parameters using mobiles’ measurements in wireless cellular network. 2012.

Zakeri, H., R.S. Shirazi, and G.J.a.p.a. Moradi, An Accurate Model to Estimate 5G Propagation Path Loss for the Indoor Environment. 2023.

Gao, H., et al., Robust beamforming for reconfigurable intelligent surface-assisted multi-cell downlink transmissions. 2024.

Gao, S., et al., Deep multi-stage CSI acquisition for reconfigurable intelligent surface aided MIMO systems. 2021. 25(6): p. 2024-2028.

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Published

2024-12-01

Issue

Section

Electrical Engineering

How to Cite

ajeel, 7haneenabdulrahman7, & Hburi, I. . (2024). Meta surface Assisted Open radio access networks. Wasit Journal of Engineering Sciences, 12(4), 1-14. https://doi.org/10.31185/ejuow.Vol12.Iss4.550

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