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Telecommunication Systems
Erschienen in: Telecommunication Systems 4/2017

26.07.2016

A support vector regression approach to detection in large-MIMO systems

verfasst von: R. Ramanathan, M. Jayakumar

Erschienen in: Telecommunication Systems | Ausgabe 4/2017

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Abstract

We propose a support vector regression approach for symbol detection in large-MIMO systems employing spatial multiplexing. We explore the applicability of machine learning algorithms, in particular support vector machines, to address one of the recent research problem in communications.The machine learning capability is exploited to achieve fast detection in large dimension systems. The performance of the proposed method is compared with lattice reduction aided detection which is currently the popular choice and the improvement in terms of bit error rate is demonstrated. The sparse formulation of the problem matrix reduces the computational complexity and enables faster detection. The proposed detection algorithm is tailored to address both uncorrelated and correlated channel conditions as well.
Vorheriger Artikel Efficient E-coupon systems with strong user privacy
Nächster Artikel Design and analysis of parallel file downloading algorithms in peer-to-peer networks

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Literatur
1.
Fredrik Rusek, D., Persson, B. K., Lau, E. G. Larsson, Marzetta, Thomas L., Ove, Edfors, & Tufvesson, Fredrik. (2013). Scaling Up MIMO: opportunities and challenges with very large arrays. IEEE Signal Processing Magazine, 30(1), 40–60.CrossRef
2.
Tanumay Datta, N., Srinidhi, A. Chockalingam, & Sundar Rajan, B. (2010). Random-restart reactive tabu search algorithm for detection in large-MIMO systems. IEEE Communications Letters, 14(12), 1107–1109.CrossRef
3.
Ramanathan, R., & Jayakumar, M. (2015). A performance study of semidefinite relaxation detector in spatially correlated and rank deficient large MIMO systems. Wireless Personal Communications, 83(4), 2883–2897.CrossRef
4.
Zhou, Q., & Ma, X. (2013). Element-based lattice reduction algorithms for large MIMO detection. IEEE Journal of Selected Areas in Communications, 31(2), 274–286.CrossRef
5.
Ramanathan, R., & Jayakumar, M. (2015). A novel cuckoo search approach to detection in spatially correlated MIMO channels. International Journal of Mathematical Modeling and Numerical Optimization, 6(2), 101–113.CrossRef
6.
Wubben, D., Seethaler, D., Jaldén, J., & Matz, G. (2011). Lattice reduction: a survey with applications in wireless communications. IEEE Signal Processing Magazine, 28(3), 70–91.CrossRef
7.
Bai, L., Chen, C., & Choi, J. (2010). Error-probability-based column reordering criterion for lattice-reduction-based list MIMO detection. Electronics Letters, 46(12), 868–869.CrossRef
8.
Bai, L., Chen, C., & Choi, J. (2012). Partial MAP-based lattice reduction-aided list MIMO detection. Electronics Letters, 48(10), 598–600.CrossRef
9.
Shao, Z. Y., Cheung, S. W., & Yuk, T. I. (2014). Lattice-reduction-aided semidefinite relaxation detection algorithms for multiple-input multiple-output systems. IET Communications, 8(4), 448–454.CrossRef
10.
Luo, Z. Q., Ma, W. K., So, A. M., Ye, Y., & Zhang, S. (2010). Semidefinite relaxation of quadratic optimization problems. IEEE Signal Processing Magazine, 27(3), 20–34.CrossRef
11.
Aly, S. (2014). Partially occluded pedestrian classification using histogram of oriented gradients and local weighted linear kernel support vector machine. IET Computer Vision, 8(6), 620–628.CrossRef
12.
Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Remaining useful life estimation based on nonlinear feature reduction and support vector regression. Engineering Applications of Artificial Intelligence, 26(7), 1751–1760.CrossRef
13.
Gryllias, K. C., & Antoniadis, I. A. (2012). A support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2), 326–344.CrossRef
14.
Han, L., Han, L., & Zhao, H. (2013). Orthogonal support vector machine for credit scoring. Engineering Applications of Artificial Intelligence, 26(2), 848–862.CrossRef
15.
Yang, H. Y., Wang, X. Y., Zhang, Y., & Miao, E. N. (2013). Robust digital watermarking in PDTDFB domain based on least squares support vector machine. Engineering Applications of Artificial Intelligence, 26(9), 2058–2072.CrossRef
16.
Decherchi, S., Gastaldo, P., Parodi, M., & Zunino, R. (2008). Low-complexity, linear circuit implementation of support vector machines training. Electronics Letters, 44(25), 1478–1479.CrossRef
17.
Liao, R. J., Zheng, H. B., Grzybowski, S., Yang, L. J., Tang, C., & Zhang, Y. Y. (2011). Fuzzy information granulated particle swarm optimisation-support vector machine regression for the trend forecasting of dissolved gases in oil-filled transformers. IET Electric Power Applications, 5(2), 230–237.CrossRef
18.
Zhou, H., Zhao, J. P., Zheng, L. G., & Cen, K. F. (2012) Modeling NOx emissions from coal-fired utility boilers using support vector regression with ant colony optimization. Engineering Applications of Artificial Intelligence, 25(1), 147–158.
19.
Santamaría, I. (2003). Design of linear-phase FIR filters using support vector regression approach. Electronics Letters, 39(19), 1422–1423.CrossRef
20.
Cheng, H., Tian, J. W., Liu, J., & Yu, Q. Z. (2004). Wavelet domain image denoising via support vector regression. Electronics Letters, 40(23), 1479–1481.CrossRef
21.
Song, P., Bao, Y. Q., Zhao, L., & Zou, C. R. (2011). Voice conversion using support vector regression. Electronics Letters, 47(18), 1045–1046.
22.
Naeem, M., & Lee, D. C. (2011). Low-complexity joint transmit and receive antenna selection for MIMO systems. Engineering Applications of Artificial Intelligence, 24(6), 1046–1051.CrossRef
23.
Wu, C., Yu, Q., & Yi, K. (2012). Least-squares support vector machine-based learning and decision making in cognitive radios. IET Communications, 6(17), 2855–2863.CrossRef
24.
Gao, Z., Dai, L., & Wang, Z. (2014). Structured compressive sensing based superimposed pilot design in downlink large-scale MIMO systems. Electronics Letters, 50(12), 896–898.CrossRef
25.
Wang, M., Cheng, X., & Zhu, X. (2015). Matching pursuit-based singular vectors estimation for large MIMO beamforming. Electronics Letters, 51(2), 56–57.CrossRef
26.
Choi, J. (2005). Iterative receivers with bit-level cancellation and detection for MIMO-BICM systems. IEEE Transactions on Signal Processing, 53(12), 4568–4577.CrossRef
27.
Gesbert, D., Bölcskei, H., Gore, D. A., & Paulraj, A. J. (2002). Outdoor MIMO wireless channels : models and performance predictio. IEEE Transactions on Communication, 50(12), 1926–1934.CrossRef
28.
Bennett, K. P., Momma, M., & Embrechts, M. J. (2002). MARK: A boosting algorithm for heterogeneous kernel models. In Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining series, KDD ’02 (pp. 24–31). doi:10.​1145/​775047.​775051.
Metadaten
Titel
A support vector regression approach to detection in large-MIMO systems
verfasst von
R. Ramanathan
M. Jayakumar
Publikationsdatum
26.07.2016
Verlag
Springer US
Erschienen in
Telecommunication Systems / Ausgabe 4/2017
Print ISSN: 1018-4864
Elektronische ISSN: 1572-9451
DOI
https://doi.org/10.1007/s11235-016-0202-2

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