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LS Interference Alignment Algorithm Based on Symbol Detection Assistance

Received: 23 December 2018     Accepted: 14 January 2019     Published: 18 April 2019
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Abstract

With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.

Published in Mathematics and Computer Science (Volume 4, Issue 1)
DOI 10.11648/j.mcs.20190401.11
Page(s) 1-5
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Interference Alignment (IA), Interference Cancellation, Least Squares (LS), Symbol Detection Assistance

References
[1] Anming Dong, Haixia Zhang, Dongfeng Yuan, Xiaotian Zhou. 2016. Interference Alignment Transceiver Design by Minimizing the Maximum Mean Square Error for MIMO Interfering Broadcast Channel.IEEE Transaction on Vehicular Technology. 65, 8(August. 2016), 3024-6036.
[2] Galymzhan Nauryzbayev, Emad Alsusa. 2016. Enhanced Multiplexing Gain Using Interference Alignment Cancellation in Multi-cell MIMO Networks. IEEE Transaction on Information Technology. 62, 1(January.2016), 357-369.
[3] Zhiyu Cheng, Natasha Devroye, Tang Liu. 2016. The Degrees of Freedom of Full-Duplex Bidirectional Interference Networks With and Without a MIMO Relay. IEEE Transaction on wireless Communications. 15, 4(April.2016), 2912-2924.
[4] Krishna Gomadam, Viveck R.Cadambe, Syed A.Jafar.2011. A Distributed Numerical Approach to Interference Alignment and Applications to Wireless Interference Networks. IEEE Transaction on Information Theory. 57, 6(June.2011), 3309-3322.
[5] Suh, C. and Tse, D. 2008. Interference Alignment for Cellular Networks [C]. 46th Annual Allerton Conference on Communications, Control and Computing (Illinois, USA, Sep. 2008).
[6] Vasilis N, Mohammad A, and Giuseppe C. Cellular Interference Alignment. IEEE Trans. Inf. Theory. 2015, 61(3), 1194-1217.
[7] K. Gomadam, V. R. Cadambe, and S. A. Jafar. A distributed numerical approach to interference alignment and applications to wireless interference networks. IEEE Trans. Inf. Theory. 57, 6(Jun. 2011), 3309-3322.
[8] Razavi S M, and Ratnarajah T. Adaptive LS and MMSE based beamformer design for multiuser MIMO interference channels. IEEE Trans. Veh. Technol. 65, 1(2016), 132-144.
[9] Hakjea Sung, Seok-Hwan, kyoung-Jae Lee. 2010. Linear Precoder Designs for K-user Interference Channels. IEEE Transactions on Wireless Communications. 9, 1(January, 2010), 291-300.
[10] Paula Aquilina, Tharmalingam Ratnarajah. 2015. Performance Analysis of IA Techniques in the MIMO IBC With Imperfect CSI.IEEE Transaction on Communications. 63, 4(April.2015), 1259-1268.
[11] Leonard H. Grokop, David N. C. Tse, Roy D. Yates. 2011. Interference Alignment for Line-of-sight Channels. IEEE Transactions on Information Theory. 57, 9(September, 2011), 5820-5832.
[12] Cheuk Ting Li, Ayfer Ozgur.2016. Channel Diversity Needed for Vector Space Interference Alignment. IEEE Transactions on Information Theory. 62, 4(April, 2016), 1942-1956.
[13] Jia, G. Q., Pan, Y., Du, J. J., and Ji, X. H. 2018. Symbol detection aided minimum mean square error interference alignment. In 2018 IEEE MTT-S International Wireless Symposium. (The Chengdu, The China, May 06-09, 2018).
Cite This Article
  • APA Style

    Guoqing Jia, Junjun Du, Xuebin Zheng. (2019). LS Interference Alignment Algorithm Based on Symbol Detection Assistance. Mathematics and Computer Science, 4(1), 1-5. https://doi.org/10.11648/j.mcs.20190401.11

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    ACS Style

    Guoqing Jia; Junjun Du; Xuebin Zheng. LS Interference Alignment Algorithm Based on Symbol Detection Assistance. Math. Comput. Sci. 2019, 4(1), 1-5. doi: 10.11648/j.mcs.20190401.11

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    AMA Style

    Guoqing Jia, Junjun Du, Xuebin Zheng. LS Interference Alignment Algorithm Based on Symbol Detection Assistance. Math Comput Sci. 2019;4(1):1-5. doi: 10.11648/j.mcs.20190401.11

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  • @article{10.11648/j.mcs.20190401.11,
      author = {Guoqing Jia and Junjun Du and Xuebin Zheng},
      title = {LS Interference Alignment Algorithm Based on Symbol Detection Assistance},
      journal = {Mathematics and Computer Science},
      volume = {4},
      number = {1},
      pages = {1-5},
      doi = {10.11648/j.mcs.20190401.11},
      url = {https://doi.org/10.11648/j.mcs.20190401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mcs.20190401.11},
      abstract = {With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.},
     year = {2019}
    }
    

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    AU  - Guoqing Jia
    AU  - Junjun Du
    AU  - Xuebin Zheng
    Y1  - 2019/04/18
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    DO  - 10.11648/j.mcs.20190401.11
    T2  - Mathematics and Computer Science
    JF  - Mathematics and Computer Science
    JO  - Mathematics and Computer Science
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    EP  - 5
    PB  - Science Publishing Group
    SN  - 2575-6028
    UR  - https://doi.org/10.11648/j.mcs.20190401.11
    AB  - With the rapid growth of network users, how to increase the system capacity has become an urgent problem for the current communication system in the case of limited spectrum resources. The introduction of multi-user systems has increased system capacity, but it has also led to inter-user interference, which has further affected system capacity. To solve the multi-user interference problem, interference alignment is introduced. Interference Alignment (IA) is an interference cancellation technique that effectively eliminates the effects of interfering signals by compressing the interfering signal into a space independent of the desired signal and then forcing the interfering signal to zero at the receiving end. However, in practical applications, interference-aligned transceivers require a joint design, which is often difficult to achieve. The traditional approach is to mathematically expect it, but it also leads to some degree of irrationality in the transceiver design. In this paper, based on the traditional least square interference alignment (LS-IA) algorithm, a symbol-detection-assisted least square interference alignment (SDA-LS-IA) algorithm is proposed for its shortcomings in transceiver algorithm design. Firstly, based on the precoding matrix and the zero-forcing matrix of the transceiver designed by the traditional LS-IA, the symbol detection is performed, and then the transceiver is designed again according to the detection symbol, and then the symbol detection is performed. The computer simulation proves that the proposed algorithm has better anti-interference performance than the traditional LS-IA.
    VL  - 4
    IS  - 1
    ER  - 

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Author Information
  • College of Physics and Electronic Information, Qinghai Nationalities University, Xining, China

  • College of Physics and Electronic Information, Qinghai Nationalities University, Xining, China

  • Mechanical and Electrical Engineering, Hebei Normal University of Science & Technology, Qinghuangdao, China

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