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Tytuł artykułu

A scalable soft richardson method for detection in a massive MIMO system

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Warianty tytułu
PL
Skalowalna metoda Richardsona do detekcji w dużych systemach MIMO
Języki publikacji
EN
Abstrakty
EN
The exponential growth in traffic data transmission rates is outpacing current technologies. To overcome these barriers, we propose to use the modified Richardson method, which offers a lower number of iterations and an optimal scalability condition for parallel architecture. Research indicates that the convergence provided by the channel hardening effect offers a good performance in Richardson detection. We then show a simulation of the proposed detector that allows the iteration methods to be set in systems with a large number of antennas.
PL
Metoda Richardsona został użyta do rozwiązania problemu wzrostu wielkości przesyłu danych. Metoda ta umożliwia użycie mniejszej liczby iteracji i optymalizację skalowalności w architekturze równoległej. Przedstawiono symulacje wykazującą że zaproponowana detekcja umożliwia iterację z dużą liczbą anten.
Rocznik
Strony
199--203
Opis fizyczny
Bibliogr. 32 poz., rys., tab., wykr.
Twórcy
  • Federal Institute of Education, Science and Technology of Rio Grande do Norte, RN 288 - Nova Caicó, ZIP 59300-000 Caicó-RN, Brazil
autor
  • Department of Electrical Engineering, University of Rio Grande do Norte, Campus Universitário - Lagoa Nova, ZIP 59072-970 Natal/RN, Brazil
Bibliografia
  • [1] J. Hoydis, S. ten Brink, and M. Debbah, “Massive mimo in the ul/dl of cellular networks: How many antennas do we need?” Selected Areas in Communications, IEEE Journal on, vol. 31, no. 2, pp. 160–171, February 2013.
  • [2] E. Larsson, O. Edfors, F. Tufvesson, and T. Marzetta, “Massive mimo for next generation wireless systems,” Communications Magazine, IEEE, vol. 52, no. 2, pp. 186–195, February 2014.
  • [3] F. Boccardi, R. Heath, A. Lozano, T. Marzetta, and P. Popovski, “Five disruptive technology directions for 5g,” Communications Magazine, IEEE, vol. 52, no. 2, pp. 74–80, February 2014.
  • [4] H. Huang, C. Papadias, and S. Venkatesan, MIMO Communication for Cellular Networks, ser. Transmission, Processing and Storage. Springer, 2011.
  • [5] E. T. Ar and I. E. Telatar, “Capacity of multiantenna gaussian channels,” European Transactions on Telecommunications, vol. 10, pp. 585–595, 1999.
  • [6] F. Rusek, D. Persson, B. K. Lau, E. Larsson, T. Marzetta, O. Edfors, and F. Tufvesson, “Scaling up mimo: Opportunities and challenges with very large arrays,” Signal Processing Magazine, IEEE, vol. 30, no. 1, pp. 40–60, Jan 2013.
  • [7] H. Q. Ngo, E. Larsson, and T. Marzetta, “Energy and spectral efficiency of very large multiuser mimo systems,” Communications, IEEE Transactions on, vol. 61, no. 4, pp. 1436–1449, April 2013.
  • [8] M.Wu, B. Yin, A. Vosoughi, C. Studer, J. Cavallaro, and C. Dick, “Approximate matrix inversion for high-throughput data detection in the large-scale mimo uplink,” in Circuits and Systems (ISCAS), 2013 IEEE International Symposium on, May 2013, pp. 2155–2158.
  • [9] J. W. Choi, B. Shim, A. Singer, and N. I. Cho, “Low-complexity decoding via reduced dimension maximum-likelihood search,” Signal Processing, IEEE Transactions on, vol. 58, no. 3, pp. 1780–1793, March 2010.
  • [10] J. Jalden and B. Ottersten, “On the complexity of sphere decoding in digital communications,” Signal Processing, IEEE Transactions on, vol. 53, no. 4, pp. 1474–1484, April 2005.
  • [11] Y. Ding, Y. Wang, J.-F. Diouris, and Z. Yao, “Robust fixedcomplexity sphere decoders for rankdeficient mimo systems,” Wireless Communications, IEEE Transactions on, vol. 12, no. 9, pp. 4297–4305, September 2013.
  • [12] Q. Zhou and X. Ma, “Element-based lattice reduction algorithms for large mimo detection,” Selected Areas in Communications, IEEE Journal on, vol. 31, no. 2, pp. 274–286, February 2013.
  • [13] K. Singhal, T. Datta, and A. Chockalingam, “Lattice reduction aided detection in large-mimo systems,” in Signal Processing Advances in Wireless Communications (SPAWC), 2013 IEEE 14th Workshop on, June 2013, pp. 594–598.
  • [14] M. M. Misiewicz, R. C. Elliott, K. R. Jacobson, and W. A. Krzymien, “Eigenmode scheduling via simulated annealing for multiuser mimo downlink with successive zero-forcing precoding,” in Wireless Communication Systems (ISWCS 2013), Proceedings of the Tenth International Symposium on, Aug 2013, pp. 1–5.
  • [15] H. Purmehdi, R. Elliott, and W. Krzymien, “Simulated annealing user scheduling for coordinated heterogeneous mimo networks,” in Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on, Nov 2012, pp. 1157–1161.
  • [16] T. Abrao, F. Ciriaco, L. Oliveira, B. Angelico, P. Jeszensky, and F. Casadevall, “Weighting particle swarm, simulation annealing and local search optimization for s/mimo mc-cdma systems,” in Swarm Intelligence Symposium, 2008. SIS 2008. IEEE, Sept 2008, pp. 1–7.
  • [17] T. Datta, N. Ashok Kumar, A. Chockalingam, and B. Rajan, “A novel mcmc algorithm for near-optimal detection in large-scale uplink mulituser mimo systems,” in Information Theory and Applications Workshop (ITA), 2012, Feb 2012, pp. 69–77.
  • [18] L. Dai, X. Gao, X. Su, S. Han, C. I, and Z. Wang, “Lowcomplexity soft-output signal detection based on gauss-seidel method for uplink multi-user large-scale mimo systems,” Vehicular Technology, IEEE Transactions on, vol. PP, no. 99, pp. 1–1, 2014.
  • [19] 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 Global Communications Conference (GLOBE-COM), 2014 IEEE, Dec 2014, pp. 3291–3295.
  • [20] X. Gao, L. Dai, C. Yuen, and Y. Zhang, “Low-complexity mmse signal detection based on richardson method for large-scale mimo systems,” in Vehicular Technology Conference (VTC Fall), 2014 IEEE 80th, Sept 2014, pp. 1–5.
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  • [23] H. Anzt, S. Tomov, P. Luszczek, W. Sawyer, and J. Dongarra, “Acceleration of GPU-based Krylov solvers via data transfer reduction,” International Journal of High Performance Computing Applications, vol. 29, no. 3, pp. 366–383, Apr. 2015.
  • [24] R. Dabeti and L. Saranovac, “Design and fpga implementation of module for space multiplexing in multi-user mimo system,” Przegld Elektrotechniczny, vol. R. 89, nr 8, pp. 162–165, 2013.
  • [25] C.-Y. Hung and T.-H. Sang, “A sphere decoding algorithm for mimo channels,” in Signal Processing and Information Technology, 2006 IEEE International Symposium on, Aug 2006, pp. 502–506.
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  • [27] T. Narasimhan and A. Chockalingam, “Channel hardeningexploiting message passing (chemp) receiver in large-scale mimo systems,” Selected Topics in Signal Processing, IEEE Journal of, vol. 8, no. 5, pp. 847–860, Oct 2014.
  • [28] A. M. Tulino and S. Verdú, “Random matrix theory and wireless communications,” Commun. Inf. Theory, vol. 1, no. 1, pp. 1–182, Jun. 2004. [Online]. Available: http://dx.doi.org/10.1516/0100000001
  • [29] Björck Ake, "Numerical Methods for Least Squares Problems", Society for Industrial and Applied Mathematics, 1996, ch. 7, pp. 269–316.
  • [30] K. S. Kim, K. Hyun, C. W. Yu, Y. O. Park, D. Yoon, and S. K. Park, “General log-likelihood ratio expression and its implementation algorithm for gray-coded qam signals,” ETRI Journal, vol. 28, no. 3, pp. 291–300, june 2006. [Online]. Available: http://dx.doi.org/10.4218/etrij.06.0105.0161
  • [31] S. Mohammed, A. Chockalingam, and B. Sundar Rajan, “A lowcomplexity precoder for large multiuser miso systems,” in Vehicular Technology Conference, 2008. VTC Spring 2008. IEEE, May 2008, pp. 797–801.
  • [32] M. Cirkic and E. G. Larsson, “SUMIS: A near-optimal softouput MIMO detector at low and fixed complexity,” CoRR, vol. abs/1207.3316, 2012.
Uwagi
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-25d67545-8db8-4350-8887-7b4cc6911546
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