Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Being a crucial aspect of fifth-generation (5G) mobile communications systems, massively multiple-input multipleoutput (mMIMO) architectures are expected to help achieve the highest key performance indicators. However, the huge numbers of antennas used in such systems make it difficult to determine the inversion of the signal channel matrix relied upon by several detection methods, hence posing a problem with accurate estimation of the symbols sent. In this paper, conjugate gradient (CG) and successive over-relaxation (SOR) methods are selected to construct a new iterative approach that avoids the matrix inversion computation issue. This suggested approach for uplink mMIMO detection is based on a joint cascade structure of both iterative methods. The CG method is first applied and adjusted for the initial solution, followed by the SOR method in the final iterations for terminal computations, resulting in an algorithm with robust performance and low computational complexity. Furthermore, the new hybrid scheme operates based on the relaxation parameter, whose value has a great impact on error performance and, whose optimal determination is necessary. Numerical simulations reveal that the proposed scheme is capable of significantly improving signal detection accuracy with minimum complexity. The simulation results indicated that the proposed detector outperforms CG and SOR detectors, achieves close to optimal performance, requires fewer iterations, and reduces complexity.
Rocznik
Tom
Strony
1--9
Opis fizyczny
Bibliogr. 26 poz., rys., tab., wykr.
Twórcy
autor
- Electrical Engineering Laboratory (LAGE), Faculty of New Information Technologies and Communication, Kasdi Merbah Ouargla University, Ouargla, Algeria
autor
- Department of Electronic and Telecommunications, Faculty of New Information Technologies and Communication, Kasdi Merbah Ouargla University, Ouargla, Algeria
Bibliografia
- [1] L.E. Ghorab, E.F. Badran, A.I. Zaki, and W.K. Badawi, "Multicarrier technique for 5G massive MIMO system based on CDMA and CMFB", Optical and Quantum Electronics, vol. 55, no. 1, Article ID 25, 2022 (https://doi.org/10.1007/s11082-022-04272-9).
- [2] X. Lin, "An overview of 5G advanced evolution in 3GPP release 18", IEEE Communications Standards Magazine, vol. 6, no. 3, pp. 77–83, 2022 (https://doi.org/10.1109/MCOMSTD.0001.2200001).
- [3] J. Lee, M. Enescu, A. Grøvlen, and Y. Zhang, "Evolution of 5G NR MIMO Standards", IEEE Communications Standards Magazine, vol. 6, no. 1, p. 12, 2022 (https://doi.org/10.1109/MCOMSTD.2022.9762868).
- [4] J. Pang et al., "A new 5G radio evolution towards 5G-Advanced", Science China Information Sciences, vol. 65, no. 9, p. 191301, 2022 (https://doi.org/10.1007/s11432-021-3470-1).
- [5] M. Fuentes et al., "5G new radio evaluation against IMT-2020 key performance indicators", IEEE Access, vol. 8, pp. 110880–110896, 2020 (https://doi.org/10.1109/ACCESS.2020.3001641).
- [6] A. Naceur, "Damped Jacobi methods based on two different matrices for signal detection in massive MIMO uplink", Journal of Microwaves, Optoelectronics and Electromagnetic Applications, vol. 20, no. 1, pp. 92–104, 2021 (https://doi.org/10.1590/2179-1074202 1v20i1889).
- [7] A. Naceur, "Initialization of an iterative low-complexity method for signal precoding in mmWave massive MIMO systems", Traitement du Signal, vol. 40, no. 1, pp. 361–366, 2023 (https://doi.org/10.18280/ts.400136).
- [8] M.A. Albreem, M. Juntti, and S. Shahabuddin, "Massive MIMO detection techniques: A survey", IEEE Communications Surveys and Tutorials, vol. 21, no. 4, pp. 3109–3132, 2019 (https://doi.org/10.1109/COMST.2019.2935810).
- [9] S. Shahabuddin, I. Hautala, M. Juntti, and C. Studer, "ADMM-based infinity-norm detection for massive MIMO: Algorithm and VLSI architecture", IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 29, no. 4, pp. 747–759, 2021 (https://doi.org/10.1109/TVLSI.2021.3056946).
- [10] Z. Zhang, Y. Li, X. Yan, and Z. Ouyang, "A low-complexity AMP detection algorithm with deep neural network for massive MIMO systems", Digital Communications and Networks, 2022 [Online]. Available: https://www.sciencedirect.com/science/arti cle/pii/S2352864822002528 (https://doi.org/10.1016/ j.dcan.2022.11.011).
- [11] I.A. Khoso, X. Dai, M.N. Irshad, A. Khan, and X. Wang, "A low-complexity data detection algorithm for massive MIMO systems", IEEE Access, vol. 7, pp. 39341–39351, 2019 (https://doi.org/10.1109/ACCESS.2019.2907366).
- [12] G. Peng, L. Liu, P. Zhang, S. Yin, and S. Wei, "Low-computing-load, high-parallelism detection method based on Chebyshev iteration for massive MIMO systems with VLSI architecture", IEEE Transactions on Signal Processing, vol. 65, no. 14, pp. 3775–3788, 2017 (https://doi.org/10.1109/TSP.2017.2698410).
- [13] M.A.M. Albreem, A.A. El-Saleh, and M. Juntti, "Linear massive MIMO uplink detector based on joint Jacobi and Gauss-Seidel methods", in 16th International Conference on the Design of Reliable Communication Networks DRCN 2020, pp. 1–4 (https://doi.org/10.1109/DRCN48652.2020.1570610672).
- [14] F. Jin, Q. Liu, H. Liu, and P. Wu, "A low complexity signal detection scheme based on improved Newton iteration for massive MIMO systems", IEEE Communications Letters, vol. 23, no. 4, pp. 748–751, 2019 (https://doi.org/10.1109/LCOMM.2019.2897798).
- [15] X. Zhao et al., "An improved Jacobi-based detector for massive MIMO systems", Information, vol. 10, no. 5, p. 165, 2019 (https://doi.org/10.3390/info10050165).
- [16] B. Kang, J.-H. Yoon, and J. Park, "Low-complexity massive MIMO detectors based on Richardson method", ETRI Journal, vol. 39, no. 3, pp. 326–335, 2017 (https://doi.org/10.4218/etrij.17.01 16.0732).
- [17] J. Minango et al., "Synchronization Reduction of the Conjugate Gradient Detector Used in Massive MIMO Uplink", in Proceedings of the 5th Brazilian Technology Symposium: Emerging Trends, Issues, and Challenges in the Brazilian Technology, vol. 1, 2020, pp. 225–233 (https://doi.org/10.1007/978-3-030-57548-9_21).
- [18] A. Yu et al., "Efficient successive over relaxation detectors for massive MIMO", IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 6, pp. 2128–2139, 2020 (https://doi.org/10.1109/TCSI.2020.2966318).
- [19] X. Qin, Z. Yan, and G. He, "A near-optimal detection scheme based on joint steepest descent and Jacobi method for uplink massive MIMO systems", IEEE Communications Letters, vol. 20, no. 2, pp. 276–279, 2016 (https://doi.org/10.1109/LCOMM.2015.2504506).
- [20] Z.M. Gebeyehu, R.S. Singh, S. Mishra, and D.S. Rathee, "Efficient hybrid iterative method for signal detection in massive MIMO up-link system over AWGN channel", Journal of Engineering, 2022, pp. 1–19 (https://doi.org/10.1155/2022/3060464).
- [21] K. Izadinasab, A.W. Shaban, and O. Damen, "Detection for hybrid beamforming millimeter wave massive MIMO systems", IEEE Com munications Letters, vol. 25, no. 4, pp. 1168–1172, 2021 (https://doi.org/10.1109/LCOMM.2020.3047994).
- [22] X. Gao, L. Dai, C. Yuen, and Y. Zhang, "Low-complexity MMSE signal detection based on Richardson method for large-scale MIMO systems", in 2014 IEEE 80th Vehicular Technology Conference (VTC2014-Fall), 2014, pp. 1–5 (https://doi.org/10.1109/VTCFall.2014.6966041).
- [23] Y. Hu, Z. Wang, X. Gaol, and J. Ning, "Low-complexity signal detection using CG method for uplink large-scale MIMO systems", in 2014 IEEE International Conference On Communication Systems, 2014, pp. 477–481 (https://doi.org/10.1109/ICCS.2014.70 24849).
- [24] X. Gao, L. Dai, Y. Hu, Z. Wang, and Z. Wang, "Matrix inversion-less signal detection using SOR method for uplink large-scale MIMO systems", in 2014 IEEE Global Communications Conference, 2014, pp. 3291–3295 (https://doi.org/10.1109/GLOCOM.2014.70 37314).
- [25] J. Minango and C.T. Pozo, "Optimal and quasi-optimal relaxation parameter for massive MIMO detector based on SOR method", in Innovation and Research-A Driving Force for Socio-Econo-Technological Development: Proceedings of the CI3 2021, Lecture Notes in Networks and Systems, vol. 511. Springer, 2022, pp. 3–10 (https://doi.org/10.1007/978-3-031-11438-0_1).
- [26] E. Björnson, J. Hoydis, and L. Sanguinetti, "Massive MIMO networks: Spectral, energy, and hardware efficiency", Foundations and Trends in Signal Processing, vol. 11, no. 3–4, pp. 154–655, 2017 (http://dx.doi.org/10.1561/2000000093).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-494d888a-88e9-4ad3-be50-36c8b5048fce