Proposta de um Algoritmo para Redução dos Cálculos da Inversa da Matriz de Filtragem para Detecção Linear de Sinais em Sistemas Uplink MIMO Massivo
Abstract
The massive Multiple-Input Multiple-Output (MIMO) is one of the most promising technologies for 5G. However, there are challenges in its practical implementation, such as high computational complexity in the inversion of the filtering matrix. Matrix inversion is an important issue that affects the performance of signal detection algorithms. A good detection algorithm for massive MIMO systems needs to estimate the filtering matrix with high accuracy and have low computational complexity for hardware implementation. To solve this problem, a linear signal detection method is proposed in this paper. The proposed algorithm uses an iterative scheme based on the secant method to estimate the inverse of the filtering matrix of the Minimum Mean-Squared Error (MMSE) algorithm. The iterative scheme has an advantage over other methods in the literature because it has two initial approximations, which ensure rapid convergence. Also, the stair matrix method is used to reduce the number of matrix operations and consequently the execution time. The results show that the proposed algorithm obtained near-optimal Bit Error Rate (BER) performance, which demonstrates that it has good precision in the estimation of the transmitted signal.
References
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Albreem, M., Juntti, M., and Shahabuddin, S. (2020). Efficient initialisation of iterative linear massive mimo detectors using a stair matrix. Electronics Letters, 56(1):50–52.
Albreem, M. A., Juntti, M., and Shahabuddin, S. (2019). Massive mimo detection techniques: A survey. IEEE Communications Surveys Tutorials, 21(4):3109–3132.
Artidiello, S., Cordero, A., Torregrosa, J., and Vassileva, M. (2020). Generalized inverses estimations by means of iterative methods with memory. Mathematics, 8(2):1–13.
Björnson, E., Hoydis, J., and Sanguinetti, L. (2017). Massive mimo networks: Spectral, energy, and hardware efficiency.
Chataut, R. and Akl, R. (2020). Systems for 5g and beyond networks—overview, recent trends, challenges, and future research direction. Sensors, 20(10):2753.
Dai, L., Gao, X., Su, X., Han, S., I, C., and Wang, Z. (2015). Low-complexity soft-output signal detection based on gauss–seidel method for uplink multiuser large-scale mimo systems. IEEE Transactions on Vehicular Technology, 64(10):4839–4845.
Gao, X., Dai, L., Ma, Y., and Wang, Z. (2014). Low-complexity near-optimal signal detection for uplink large-scale mimo systems. Electronics Letters, 50(18):1326–1328.
Jiang, F., Li, C., Gong, Z., and Su, R. (2018). Stair matrix and its applications to massive mimo uplink data detection. IEEE Transactions on Communications, 66(6):2437–2455.
Jin, F., Liu, Q., Liu, H., and Wu, P. (2019). A low complexity signal detection scheme based on improved newton iteration for massive mimo systems. IEEE Communications Letters, 23(4):748–751.
Khan, R., Kumar, P., Jayakody, D. N. K., and Liyanage, M. (2020). A survey on security and privacy of 5g technologies: Potential solutions, recent advancements, and future directions. IEEE Communications Surveys Tutorials, 22(1):196–248.
Lee, Y. (2017). Decision-aided jacobi iteration for signal detection in massive mimo systems. Electronics Letters, 53(23):1552–1554.
Lee, Y. (2019). Hybrid kaczmarz and coordinate-descent iterations for signal detection in massive mimo systems. Electronics Letters, 55(11):665–667.
Lu, H. (1999). Stair matrices and their generalizations with applications to iterative methods i: A generalization of the successive overrelaxation method. SIAM Journal on Numerical Analysis, 37(1):1–17.
Minango, J. and de Almeida, C. (2018). Low complexity zero forcing detector based on newton-schultz iterative algorithm for massive mimo systems. IEEE Transactions on Vehicular Technology, 67(12):11759–11766.
Naceur, A. (2021). Damped jacobi methods based on two different matrices for signal detection in massive mimo uplink. Journal of Microwaves, Optoelectronics and Electromagnetic Applications, 20(1):92–104.
Qin, X., Yan, Z., and He, G. (2016). A near-optimal detection scheme based on joint steepest descent and jacobi method for uplink massive mimo systems. IEEE Communications Letters, 20(2):276–279.
Shahabuddin, S., Juntti, M., and Studer, C. (2017). Admm-based innity norm detection for large mu-mimo: Algorithm and vlsi architecture. 2017 IEEE International Symposium on Circuits and Systems (ISCAS), pages 1–4.
Studer, C., Fateh, S., and Seethaler, D. (2011). Asic implementation of soft-input softoutput mimo detection using mmse parallel interference cancellation. IEEE Journal of Solid-State Circuits, 46(7):1754–1765.
Tang, C., Liu, C., Yuan, L., and Xing, Z. (2016). High precision low complexity matrix IEEE inversion based on newton iteration for data detection in the massive mimo. Communications Letters, 20(3):490–493.
Wu, M., Dick, C., Cavallaro, J. R., and Studer, C. (2016). High-throughput data detection for massive mu-mimo-ofdm using coordinate descent. IEEE Transactions on Circuits and Systems I: Regular Papers, 63(12):2357–2367.
Wu, M., Yin, B., Vosoughi, A., Studer, C., Cavallaro, J. R., and Dick, C. (2013). Approximate matrix inversion for high-throughput data detection in the large-scale mimo uplink. 2013 IEEE International Symposium on Circuits and Systems (ISCAS), pages 2155–2158.
Wu, M., Yin, B., Wang, G., Dick, C., Cavallaro, J. R., and Studer, C. (2014). Large-scale mimo detection for 3gpp lte: Algorithms and fpga implementations. IEEE Journal of Selected Topics in Signal Processing, 8(5):916–929.
Yakhelef, M. and Saidi, L. (2020). Low-complexity iterative detection based on parametric aor for uplink massive mimo systems. Telecommunications and Radio Engineering, 79(18):1609–1623.
Yin, B., Wu, M., Cavallaro, J. R., and Studer, C. (2014). Conjugate gradient-based softoutput detection and precoding in massive mimo systems. 2014 IEEE Global Communications Conference, pages 3696–3701.
Yin, B., Wu, M., Studer, C., Cavallaro, J. R., and Dick, C. (2013). Implementation trade-offs for linear detection in large-scale mimo systems. 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 2679–2683.
Zhang, J., Björnson, E., Matthaiou, M., Ng, D. W. K., Yang, H., and Love, D. J. (2020). Prospective multiple antenna technologies for beyond 5g. IEEE Journal on Selected Areas in Communications, 38(8):1637–1660.
Published
2021-08-16
How to Cite
SILVA, Maykon Renan Pereira da; ROCHA, Flávio Geraldo Coelho.
Proposta de um Algoritmo para Redução dos Cálculos da Inversa da Matriz de Filtragem para Detecção Linear de Sinais em Sistemas Uplink MIMO Massivo. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 462-475.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16740.
