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Open Access

Performance evaluation of DHRR-RIS based HP design using machine learning algorithms

Department of Electronics and Telecommunication Engineering, Bangalore Institute of Technology, Visvesvaraya Technological University, Bangalore 560004, India
Department of Electronics and Communication Engineering, Bangalore Institute of Technology, Visvesvaraya Technological University, Bangalore 560004, India
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Abstract

Reconfigurable Intelligent Surfaces (RIS) have emerged as a promising technology for improving the reliability of massive MIMO communication networks. However, conventional RIS suffer from poor Spectral Efficiency (SE) and high energy consumption, leading to complex Hybrid Precoding (HP) designs. To address these issues, we propose a new low-complexity HP model, named Dynamic Hybrid Relay Reflecting RIS based Hybrid Precoding (DHRR-RIS-HP). Our approach combines active and passive elements to cancel out the downsides of both conventional designs. We first design a DHRR-RIS and optimize the pilot and Channel State Information (CSI) estimation using an adaptive threshold method and Adaptive Back Propagation Neural Network (ABPNN) algorithm, respectively, to reduce the Bit Error Rate (BER) and energy consumption. To optimize the data stream, we cluster them into private and public streams using Enhanced Fuzzy C-Means (EFCM) algorithm, and schedule them based on priority and emergency level. To maximize the sum rate and SE, we perform digital precoder optimization at the Base Station (BS) side using Deep Deterministic Policy Gradient (DDPG) algorithm and analog precoder optimization at the DHRR-RIS using Fire Hawk Optimization (FHO) algorithm. We implement our proposed work using MATLAB R2020a and compare it with existing works using several validation metrics. Our results show that our proposed work outperforms existing works in terms of SE, Weighted Sum Rate (WSR), and BER.

Keywords

Reconfigurable Intelligent Surfaces (RIS)Dynamic Hybrid Relay Reflecting (DHRR)-RISMulti User Multiple Input Multiple Output (MU-MIMO)hybrid precodermachine learning and deep learning algorithmschannel state estimation

References

[1]
A. S. Gharagezlou, M. Nangir, N. Imani, and E. Mirhosseini, Energy efficient power allocation in massive MIMO systems with power limited users, in Proc. 4th Int. Conf. Telecommunications and Communication Engineering (ICTCE), Singapore, 2020, pp. 35–46.
Crossref
[2]
G. C. Alexandropoulos, I. Vinieratou, M. Rebato, L. Rose, and M. Zorzi, Uplink beam management for millimeter wave cellular MIMO systems with hybrid beamforming, in Proc. 2021 IEEE Wireless Communications and Networking Conf. (WCNC), Nanjing, China, 2021, pp. 1–7.
Crossref
[3]

A. Singh and S. Joshi, A survey on hybrid beamforming in MmWave massive MIMO system, J. Sci. Res., vol. 65, no. 1, pp. 201–213, 2021.
Crossref Google Scholar
[4]
J. Palacios, N. González-Prelcic, C. Mosquera, and T. Shimizu, A dynamic codebook design for analog beamforming in MIMO LEO satellite communications, in Proc. 2022 IEEE Int. Conf. Communications (ICC), Seoul, Republic of Korea, 2022, pp. 1–6.
Crossref
[5]
M. Mahmood, A. Koc, and T. Le-Ngoc, Massive-MIMO hybrid precoder design using few-bit DACs for 2D antenna array structures, in Proc. 2021 IEEE Int. Conf. Communications (ICC), virtual, 2021, pp. 1–5.
Crossref
[6]
E. Balti, Hybrid precoding for mmWave V2X doubly-selective multiuser MIMO systems, arXiv preprint arXiv: 2103.09444, 2021.
[7]

X. Zhang and F. Zhao, Hybrid precoding algorithm for millimeter-wave massive MIMO systems with subconnection structures, Wirel. Commun. Mob. Comput., vol. 2021, p. 5532939, 2021.
Crossref Google Scholar
[8]
A. J. Ortega, OMP-based hybrid precoding and SVD-based hybrid combiner design with partial CSI for massive MU-MIMO mmWave system, in Proc. 2020 Int. Conf. Communications, Signal Processing, and their Applications (ICCSPA), Sharjah, United Arab Emirates, 2021, pp. 1–5.
Crossref
[9]

H. Ayad, M. Y. Bendimerad, and F. T. Bendimerad, Hardware phase shift hybrid precoding designs for multi-user massive MIMO systems, J. Phys.: Conf. Ser., vol. 2134, p. 012027, 2021.
Crossref Google Scholar
[10]
B. Al-Nahhas, M. Obeed, A. Chaaban, and M. J. Hossain, RIS-aided cell-free massive MIMO: Performance analysis and competitiveness, in Proc. 2021 IEEE Int. Conf. Communications Workshops (ICC Workshops), Montreal, Canada, 2021, pp. 1–6.
Crossref
[11]
I. Yildirim, A. Koc, E. Basar, and T. Le-Ngoc, RIS-aided angular-based hybrid beamforming design in mmWave massive MIMO systems, in Proc. 2022 IEEE Global Communications Conf. (GLOBECOM), Rio de Janeiro, Brazil, 2023, pp. 5267–5272.
Crossref
[12]
M. Cui, Z. Wu, Y. Chen, S. Xu, F. Yang, and L. Dai, Demo: low-power communications based on RIS and AI for 6G, in Proc. 2022 IEEE Int. Conf. Communications Workshops (ICC Workshops), Seoul, Republic of Korea, 2022, pp. 1–2.
Crossref
[13]
L. Hu, G. Li, X. Qian, D. W. Kwan Ng, and A. Hu, Joint transmit and reflective beamforming for RIS-assisted secret key generation, in Proc. 2022 IEEE Global Communications Conf. (GLOBECOM), Rio de Janeiro, Brazil, 2023, pp. 2352–2357.
Crossref
[14]
Q. Zhu, H. Li, R. Liu, M. Li, and Q. Liu, Hybrid beamforming and passive reflection design for RIS-assisted mmWave MIMO systems, in Proc. 2021 IEEE Int. Conf. Communications Workshops (ICC Workshops), Montreal, Canada, 2021, pp. 1–6.
Crossref
[15]
K. Liu, Z. Zhang, and L. Dai, User-side RIS: Realizing large-scale array at user side, in Proc. 2021 IEEE Global Communications Conference (GLOBECOM), Madrid, Spain, 2022, pp. 01–06.
Crossref
[16]
R. Schroeder, J. He, and M. Juntti, Passive RIS vs. hybrid RIS: A comparative study on channel estimation, in Proc. 2021 IEEE 93rd Vehicular Technology Conf. (VTC2021-Spring), Helsinki, Finland, 2021, pp. 1–7.
Crossref
[17]
Y. Zhu, Z. Bo, M. Li, Y. Liu, Q. Liu, Z. Chang, and Y. Hu, Deep reinforcement learning based joint active and passive beamforming design for RIS-assisted MISO systems, in Proc. 2022 IEEE Wireless Communications and Networking Conf. (WCNC), Austin, TX, USA, 2022, pp. 477–482.
Crossref
[18]
N. T. Nguyen, Q. D. Vu, K. Lee, and M. Juntti, Spectral efficiency optimization for hybrid relay-reflecting intelligent surface, in Proc. 2021 IEEE Int. Conf. Communications Workshops (ICC Workshops), Montreal, Canada, 2021, pp. 1–6.
Crossref
[19]
N. T. Nguyen, Q. D. Vu, K. Lee, and M. Juntti, Hybrid relay-reflecting intelligent surface-assisted wireless communication, arXiv preprint arXiv: 2103.03900, 2021.
[20]
N. T. Nguyen, J. He, V. D. Nguyen, H. Wymeersch, D. W. K. Ng, R. Schober, S. Chatzinotas, and M. Juntti, Hybrid relay-reflecting intelligent surface-aided wireless communications: Opportunities, challenges, and future perspectives, arXiv preprint arXiv: 2104.02039, 2021.
[21]

Y. Lu, M. Hao, and R. MacKenzie, Reconfigurable intelligent surface based hybrid precoding for THz communications, Intelligent and Converged Networks, vol. 3, no. 1, pp. 103–118, 2022.
Crossref Google Scholar
[22]

H. Niu, Z. Chu, F. Zhou, C. Pan, D. W. K. Ng, and H. X. Nguyen, Double intelligent reflecting surface-assisted multi-user MIMO mmwave systems with hybrid precoding, IEEE Trans. Veh. Technol., vol. 71, no. 2, pp. 1575–1587, 2022.
Crossref Google Scholar
[23]

C. Huang, Z. Yang, G. C. Alexandropoulos, K. Xiong, L. Wei, C. Yuen, Z. Zhang, and M. Debbah, Multi-hop RIS-empowered terahertz communications: A DRL-based hybrid beamforming design, IEEE J. Sel. Areas Commun., vol. 39, no. 6, pp. 1663–1677, 2021.
Crossref Google Scholar
[24]

L. Dai and X. Wei, Distributed machine learning based downlink channel estimation for RIS assisted wireless communications, IEEE Trans. Commun., vol. 70, no. 7, pp. 4900–4909, 2022.
Crossref Google Scholar
[25]
J. Ye, S. Guo, and M. -S. Alouini, Joint reflecting and precoding designs for SER minimization in reconfigurable intelligent surfaces assisted MIMO systems, IEEE Trans. Wirel. Commun., vol. 19, no. 8, pp. 5561–5574, 2020.
Crossref
[26]

Z. Zhou, N. Ge, Z. Wang, and L. Hanzo, Joint transmit precoding and reconfigurable intelligent surface phase adjustment: A decomposition-aided channel estimation approach, IEEE Trans. Commun., vol. 69, no. 2, pp. 1228–1243, 2021.
Crossref Google Scholar
[27]

Y. Wang, X. Chen, Y. Cai, and L. Hanzo, RIS-aided hybrid massive MIMO systems relying on adaptive-resolution ADCs: Robust beamforming design and resource allocation, IEEE Trans. Veh. Technol., vol. 71, no. 3, pp. 3281–3286, 2022.
Crossref Google Scholar
[28]

Q. Hu, Y. Cai, K. Kang, G. Yu, J. Hoydis, and Y. C. Eldar, Two-timescale end-to-end learning for channel acquisition and hybrid precoding, IEEE J. Sel. Areas Commun., vol. 40, no. 1, pp. 163–181, 2022.
Crossref Google Scholar
[29]

Q. Sun, H. Zhao, J. Wang, and W. Chen, Deep learning-based joint CSI feedback and hybrid precoding in FDD mmWave massive MIMO systems, Entropy, vol. 24, no. 4, pp. 441, 2022.
Crossref Google Scholar
[30]

X. Bao, W. Feng, J. Zheng, and J. Li, Deep CNN and equivalent channel based hybrid precoding for mmWave massive MIMO systems, IEEE Access, vol. 8, pp. 19327–19335, 2020.
Crossref Google Scholar
[31]

X. Li, Y. Huang, W. Heng, and J. Wu, Machine learning-inspired hybrid precoding for mmWave MU-MIMO systems with domestic switch network, Sensors, vol. 21, no. 9, pp. 3019, 2021.
Crossref Google Scholar
[32]

W. Ma, C. Qi, Z. Zhang, and J. Cheng, Sparse channel estimation and hybrid precoding using deep learning for millimeter wave massive MIMO, IEEE Trans. Commun., vol. 68, no. 5, pp. 2838–2849, 2020.
Crossref Google Scholar
[33]

K. M. Attiah, F. Sohrabi, and W. Yu, Deep learning for channel sensing and hybrid precoding in TDD massive MIMO OFDM systems, IEEE Trans. Wirel. Commun., vol. 21, no. 12, pp. 10839–10853, 2022.
Crossref Google Scholar
[34]

Q. Lu, T. Lin, and Y. Zhu, Channel estimation and hybrid precoding for millimeter wave communications: A deep learning-based approach, IEEE Access, vol. 9, pp. 120924–120939, 2021.
Crossref Google Scholar
[35]
I. S. Kim and J. Choi, Spatial wideband channel estimation for mmWave massive MIMO systems with hybrid architectures and low-resolution ADCs, IEEE Trans. Wirel. Commun., vol. 20, no. 6, pp. 4016–4029, 2021.
Crossref
[36]

Y. Zhang, X. Dong, and Z. Zhang, Machine learning-based hybrid precoding with low-resolution analog phase shifters, IEEE Commun. Lett., vol. 25, no. 1, pp. 186–190, 2021.
Crossref Google Scholar
[37]

X. Zhu, A. Koc, R. Morawski, and T. Le-Ngoc, A deep learning and geospatial data-based channel estimation technique for hybrid massive MIMO systems, IEEE Access, vol. 9, pp. 145115–145132, 2021.
Crossref Google Scholar
[38]

J. Shi, W. Wang, X. Yi, X. Gao, and G. Y. Li, Deep learning-based robust precoding for massive MIMO, IEEE Trans. Commun., vol. 69, no. 11, pp. 7429–7443, 2021.
Crossref Google Scholar
[39]

Q. Wang, K. Feng, X. Li, and S. Jin, PrecoderNet: hybrid beamforming for millimeter wave systems with deep reinforcement learning, IEEE Wirel. Commun. Lett., vol. 9, no. 10, pp. 1677–1681, 2020.
Crossref Google Scholar
[40]

K. Wei, J. Xu, W. Xu, N. Wang, and D. Chen, Distributed neural precoding for hybrid mmWave MIMO communications with limited feedback, IEEE Commun. Lett., vol. 26, no. 7, pp. 1568–1572, 2022.
Crossref Google Scholar
Intelligent and Converged Networks
Volume 4 Issue 3,
September 2023
Pages 237-260
DOI: 10.23919/ICN.2023.0019
Cite this article:
N G GK, M N SRR. Performance evaluation of DHRR-RIS based HP design using machine learning algorithms. Intelligent and Converged Networks, 2023, 4(3): 237-260. https://doi.org/10.23919/ICN.2023.0019

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Received: 05 March 2023
Revised: 05 May 2023
Accepted: 15 June 2023
Published: 30 September 2023
© All articles included in the journal are copyrighted to the ITU and TUP.

This work is available under the CC BY-NC-ND 3.0 IGO license:https://creativecommons.org/licenses/by-nc-nd/3.0/igo/
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