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Successive-Interference-Cancellation-Inspired Multi-user MIMO Detector Driven by Genetic Algorithm

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Theory and Applications of Dependable Computer Systems (DepCoS-RELCOMEX 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1173))

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Abstract

Multi-User Multiple-Input Multiple-Output (MU-MIMO) configuration is one of the most promising solutions to the fundamental problem of a telecommunication system: limited bandwidth. According to the MU-MIMO principles, different users transmit their signals concurrently at the same channel. It helps exploit channel capacity to a larger extent, but causes a harmful intra-channel interference at the same time. The receiver’s ability to combat the interference and retrieve individual users’ signals (Multi-User Detection, MUD) is a measure of the system dependability. In this paper we re-visit the solution to the MUD problem, based on the use of Genetic Algorithm (GA). The novelty of the current contribution is a re-designed method to generate the initial GA population, which improves the performance at no extra computational cost in comparison with the previous proposal.

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References

  1. Ganti, R., Baccelli, F., Andrews, J.: Series expansion for interference in wireless networks. IEEE Trans. Inform. Theory 58(4), 2194–2205 (2012)

    Article  MathSciNet  Google Scholar 

  2. Blomer, J., Jindal, N.: Transmission capacity of wireless ad hoc networks: Successive interference cancellation vs. joint detection. In: IEEE International Conference on Communication, ICC, Dresden. IEEE (2009)

    Google Scholar 

  3. Jaldén, J.: Maximum likelihood detection for the linear MIMO channel. Ph.D. dissertation, Royal Institute of Technology, Stockholm (2004)

    Google Scholar 

  4. Zhang, X., Haenggi, M.: The performance of successive interference cancellation in random wireless networks. IEEE Trans. Inf. Theory 60(10), 6368–6388 (2014)

    Article  MathSciNet  Google Scholar 

  5. He, J., Tang, Z., Ding, Z., Wu, D.: Successive interference cancellation and fractional frequency reuse for LTE uplink communications. IEEE Trans. Veh. Technol. 67(11), 10528–10542 (2018)

    Article  Google Scholar 

  6. Mo, Y., Goursaud, C., Gorce, J.M.: On the benefits of successive interference cancellation for ultra narrow band networks: theory and application to IoT. In: IEEE International Conference on Communication, ICC, Paris. IEEE (2017)

    Google Scholar 

  7. Khafaji, M.J., Krasicki, M.: Genetic-algorithm-driven MIMO multi-user detector for wireless communications. In: Zamojski, W. et al. (eds.) Contemporary Complex Systems and Their Dependability: Proceedings of the 13th International Conference on Dependability and Complex Systems DepCoS-RELCOMEX, vol. 761, pp. 258–269. Springer, Cham (2019)

    Google Scholar 

  8. Ng, S., Leung, S., Chung, C., Luk, A., Lau, W.: The genetic search approach: a new learning algorithm for adaptive IIR filtering. IEEE Signal Process. Mag. 13(6), 38–46 (1996)

    Article  Google Scholar 

  9. Mitchell, M.: An Introduction to Genetic Algorithms. The MIT Press, Cambridge (1996)

    MATH  Google Scholar 

  10. Liu, S., St-Hilaire, M.: A genetic algorithm for the global planning problem of UMTS networks. In: IEEE Global Telecommunication Conference, GLOBECOM, Miami. IEEE (2010)

    Google Scholar 

  11. Obaidullah, K., Siriteanu, C., Yoshizawa, S., Miyanaga, Y.: Evaluation of genetic algorithm-based detection for correlated MIMO fading channels. In: 11th International Symposium on Communications and Information Technologies, ISCIT 2011, Hongzou, pp. 507–511. IEEE (2011)

    Google Scholar 

  12. Yang, C., Han, J., Li, Y., Xu, X.: Self-adaptive Genetic algorithm based MU-MIMO scheduling scheme. In: International Conference on Communication Technology, ICCT 2013, Guilin, pp. 180–185. IEEE (2013)

    Google Scholar 

  13. Simon, D.: Evolutionary Optimization Algorithms. Wiley, Hoboken (2013)

    Google Scholar 

  14. Lipowski, A., Lipowska, D.: Roulette-wheel selection via stochastic acceptance. Physica A 391(6), 2193–2196 (2012)

    Article  Google Scholar 

Download references

Acknowledgement

The presented work has been funded by the Polish Ministry of Science and Higher Education under the research grant No. 0312/SBAD/8147.

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Authors and Affiliations

  1. Faculty of Computing and Telecommunications, Institute of Radiocommunications, Poznan University of Technology, Polanka 3, 61-131, Poznan, Poland

    Mohammed J. Khafaji & Maciej Krasicki

Authors
  1. Mohammed J. Khafaji
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  2. Maciej Krasicki
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Corresponding author

Correspondence to Maciej Krasicki .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wrocław, Poland

    Wojciech Zamojski

  2. Wrocław University of Science and Technology, Wrocław, Poland

    Jacek Mazurkiewicz

  3. Wrocław University of Science and Technology, Wrocław, Poland

    Jarosław Sugier

  4. Wrocław University of Science and Technology, Wrocław, Poland

    Tomasz Walkowiak

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

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Khafaji, M.J., Krasicki, M. (2020). Successive-Interference-Cancellation-Inspired Multi-user MIMO Detector Driven by Genetic Algorithm. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds) Theory and Applications of Dependable Computer Systems. DepCoS-RELCOMEX 2020. Advances in Intelligent Systems and Computing, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-030-48256-5_31

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