Proposta de um Algoritmo para Redução dos Cálculos da Inversa da Matriz de Filtragem para Detecção Linear de Sinais em Sistemas Uplink MIMO Massivo
Resumo
O sistema MIMO massivo é uma das tecnologias mais promissoras para as redes 5G. Entretanto, existem problemas desafiadores na implementação prática desta tecnologia, como por exemplo, a alta complexidade computacional na inversão da matriz de filtragem. A inversão de matriz é uma questão importante que afeta o desempenho dos algoritmos de detecção de sinais. Um bom algoritmo de detecção para sistemas MIMO massivo depende de uma boa precisão na estimação da matriz de filtragem e, principalmente, ter baixa complexidade computacional para implementação em hardware. Com o objetivo de solucionar este problema, propõe-se um método de detecção linear de sinais. O algoritmo proposto utiliza um esquema iterativo baseado no método da secante para estimar a inversa da matriz de filtragem do algoritmo MMSE. O esquema iterativo tem como vantagem em relação a outros métodos da literatura, ter duas aproximações iniciais, o que garante uma rápida convergência. Além disso, o método matriz escada é utilizado com o objetivo de reduzir o número de operações matriciais e, consequentemente, o tempo de execução. Os resultados mostram que o algoritmo proposto obteve desempenho quase-ótimo em termos de BER, o que demonstra que o mesmo possui uma boa precisão na estimação do sinal transmitido.
Referências
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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.
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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.
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.
Publicado
16/08/2021
Como Citar
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: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (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.
