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Linear Precoding with User and Transmit Antenna Selection

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Abstract

The major challenge in the current scenario of wireless system is increasing number of users and hence increased co-channel interference within the limited spectrum available for communication. The compromise in terms of user quality and reliability of communication system is evitable from increased number of call drops and busy channels during mobile voice calls. The above mentioned challenge is attributed for increase in the number of users accommodated in the defined spectrum. The paper presents a novel user selection and transmit antenna selection based approach for enhanced reliability in multi-user multiple input multiple output system, which is next generation wireless system. In contrast to existing technique for multiple antennas and which is evolution of regular channel inversion with block diagonalization, the proposed technique considers systematic and optimum deployment of user selection in the system to enhance sum rate or the system capacity. The comparison of algorithms viz. random, norm based and capacity based user selection is presented with its implementation with precoding techniques which is used to minimize co-channel interference. The analysis proposes that, for each selected user if the transmit antennas are chosen with presented algorithm, the sum rate is improved by 17%. Also, the bit error rate performance of linear precoding with user selection is equivalent to non-linear precoding without user selection.

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References

  1. Larsson, E. G., Tufvesson, F., Edfors, O., & Marzetta, T. L. (2014). Massive MIMO for next generation wireless systems. IEEE Communication Magazine,52(2), 186–195.

    Article  Google Scholar 

  2. Urosevic, U. & Veljovic, Z. (2016). Distributed MIMO solutions for peer-to-peer communications in future wireless systems. 24th Telecommunication Forum, pp. 1–4.

  3. Lu, L., Li, Y., Lee, A., Ashikhmin, A., & Zhang, R. (2014). An overview of massive MIMO: benefits and challenges. IEEE Journal of Selected Topics in Signal Processing,8(5), 742–758.

    Article  Google Scholar 

  4. Ngo, H. Q., Larsson, E. G., & Marzetta, T. L. (2013). Energy and spectral efficiencyof very large multiuser MIMO systems. IEEE Transactions on Communications,61(4), 1436–1449.

    Article  Google Scholar 

  5. Rusek, F., Persson, D., Lau, B., Larsson, E., Marzetta, T. L., Edfors, O., et al. (2013). Scaling up MIMO: Opportunities and challenges with very large arrays. IEEE Signal Processing Magazine,30(1), 40–60.

    Article  Google Scholar 

  6. Marzetta, T. (2010). Noncooperative cellular wireless with unlimited numbers of base station antennas. IEEE Transactions on Wireless Communication,9(11), 3590–3600.

    Article  Google Scholar 

  7. Heath, R. W., Airy, M., & Paulraj, A. J. (2001). Multiuser diversity for MIMO wireless systems with linear receivers. In Proceedings of 35th asilomar conference on signals, systems and computers (Vol. 8, pp. 1194–1199).

  8. Lyu, T. K., & Wang, X. (2015). Capacity of precoding for MU-MIMO systems. In International conference on information and communication technology convergence (ICTC) (pp. 405–407).

  9. Weingarten, H., Steinberg, Y., & Shamai, S. (2006). The capacity region of the gaussian multiple-input multiple-output broadcast channel. IEEE Transactions on Information Theory,52(9), 3936–3964.

    Article  MathSciNet  Google Scholar 

  10. Gershman, A., Sidiropoulos, N., Shahbazpanahi, S., Bengtsson, M., & Ottersten, B. (2010). Convex optimization-based beamforming. IEEE Signal Processing Magazine,27(3), 62–75.

    Article  Google Scholar 

  11. Peel, C. B., Hochwald, B. M., & Swindlehurst, A. L. (2005). A vector perturbation technique for near-capacity multiantenna multiuser communication-part I: Channel inversion and regularization. IEEE Transactions on Communications,53(1), 195–202.

    Article  Google Scholar 

  12. Tataria, H., Smith, P. J., Greenstein, L. J., & Dmochowski, P. A. (2017). Zero-forcing precoding performance in multi-user MIMO systems with heterogenous ricean fading. IEEE Wireless Communication Letters,6(1), 74–77.

    Google Scholar 

  13. Tataria, H., Smith, P. J., Greenstein, L. J., Dmochowski, P. A., & Shafi, M. (2016). Performance and analysis of downlink multiuser MIMO systems with regularized zero-forcing precoding in Ricean fading channels. In Proceedings of IEEE international conference on communications (ICC) (pp. 1185–1191).

  14. Spencer, Q. H., Swindlehurst, A. L., & Haardt, M. (2004). Zero-forcing methods for downlink spatial multiplexing in multiuser MIMO channels. IEEE Transactions on Signal Processing,52(2), 461–471.

    Article  MathSciNet  Google Scholar 

  15. Kaltenberger, F., Kountouris, M., Cardoso, L., Knopp, R., & Gesbert, D. (2008). Capacity of linear multi-user MIMO precoding schemes with measured channel data. In IEEE 9th workshop on signal processing advances in wireless communications (pp. 580–584).

  16. Boccardi, F., & Huang, H. (2007).A near-optimum technique using linear precoding for the MIMO broadcast channel. In Proceedings of IEEE ICASSP (Vol. 3, pp. 17–20).

  17. Chen, R., Shen, Z., Andrews, J., & Heath, R. (2008). Multimode transmission for multiuser MIMO systems with block diagonalization. IEEE Transactions on Signal Processing,56(7), 3294–3302.

    Article  MathSciNet  Google Scholar 

  18. Zukang, S., Runhua, C., Andrews, J. G., Heath, R. W., & Evans, B. L. (2006). Low complexity user selection algorithms for multiuser mimo systems with block diagonalization. IEEE Transactions on Signal Processing,54(9), 3658–3663.

    Article  Google Scholar 

  19. Bjornson, E., Bengtsson, M., & Ottersten, B. (2013). Receive combining vs. multistream multiplexing in downlink systems with multi-antenna users. IEEE Transactions on Signal Processing,61(13), 1–16.

    Article  Google Scholar 

  20. Yang, P., Xiao, Y., Guan, Y. L., Li, S., & Hanzo, L. (2016). Transmit antenna selection for multiple-input multiple-output spatial modulation schemes. IEEE Transactions on Communications,64(5), 2035–2048.

    Article  Google Scholar 

  21. Chen, R., Andrew, J. G., & Health, R. W. (2007). Efficient transmit antenna selection for multi-user MIMO system with block diagonalization. In Proceedings of IEEE GLOBECOM 2007 (pp. 3499–3503).

  22. Simon, M. K., & Alouini, M. S. (2000). Digital communication over fading channels: A unified approach to performance analysis. Hoboken: Wiley.

    Book  Google Scholar 

  23. Foschini, G., & Gans, M. (1998). On limits of wireless communications in a fading environment when using multiple antennas. Wireless Personal Communications,6(3), 311–335.

    Article  Google Scholar 

  24. Bjornson, E., Bengtsson, M., & Ottersten, B. (2014). Optimal multiuser transmit beamforming: A difficult problem with a simple solution structure. IEEE Signal Processing Magazine,31(4), 142–148.

    Article  Google Scholar 

  25. Choo, Y. S., Kim, J., Yang, W. Y., & Kang, C. G. (2010). MIMO-OFDM wireless communications with MATLAB. Singapore: Wiley.

    Book  Google Scholar 

  26. Sanguenetti, L., Bjornson, E., Debbah, M., & Moustakas, A. L. (2014). Optimal linear precoding in multiuser MIMO systems: A large system analysis. In Proceedings of IEEE Global communications conference (GLOBECOM) (pp. 3922–3927).

  27. Joham, M., Utschick, W., & Nossek, J. A. (2005). Linear transmit processing in MIMO communications systems. IEEE Transactions on Signal Processing,53(8), 2700–2712.

    Article  MathSciNet  Google Scholar 

  28. Lyu, T. K. (2016). Capacity of multi-user MIMO systems using MMSE and ZF precoding. In IEEE conference on computer communications Workshops (pp. 1083–1084).

  29. Choi, R., & Murch, R. (2003). A transmit preprocessing technique for multiuser MIMO systems using a decomposition approach. IEEE Transactions on Wireless Communications,2(2), 20–24.

    Article  Google Scholar 

  30. Yoo, T., & Goldsmith, A. (2006). On the optimality of multiantenna broadcast scheduling using zero-forcing beamforming. IEEE Journal on Selected Areas of Communication,24(3), 528–541.

    Article  Google Scholar 

  31. Khan, A., Vesilo, R., & Collings, I. B. (2007). Efficient user selection algorithms for wireless broadcast channels. In Wireless broadband and ultra wideband communications (pp. 27–30).

  32. Dimic, G., & Sidiropoulos, N. (2005). On downlink beamforming with greedy user selection: performance analysis and a simple new algorithm. IEEE Transactions on Signal Processing,53(10), 3857–3868.

    Article  MathSciNet  Google Scholar 

  33. Naeem, M., Bashir, S., Khan, M. U., & Syed, A. A. (2016). Performance comparison of scheduling algorithms for MU-MIMO systems. In 13th International Bhurban conference on applied sciences and technology (pp. 601–606).

  34. Essloaui, M., & Essaaidi, M. (2013). An efficient user scheduling scheme for downlink multi-user MIMO-OFDM system with block-diagonalization. International Journal of Advanced Computer Science and Applications,4(5), 100–106.

    Google Scholar 

  35. Khan, M. A., V esilo, R., Davis, L. M., & Collings, I. B. (2008). User and transmit antenna selection for MIMO broadcast wireless channels with linear receivers. In Australasian telecommunication networks and applications conference (pp. 276–281).

  36. Alkhansari, M. G., & Gershman, A. B. (2004). Fast antenna subset selection in MIMO systems. IEEE Transactions on Signal Processing,52(2), 339–347.

    Article  MathSciNet  Google Scholar 

  37. Chen, R., Heath, R. W., & Andrews, J. G. (2007). Transmit selection diversity for unitary precoded multiuser spatial multiplexing systems withlinear receivers. IEEE Transactions on Signal Processing,55(3), 1159–1171.

    Article  MathSciNet  Google Scholar 

  38. Zhong, W., & Xu, Y. (2009). Joint user and transmit antenna selection for uplink multi-user MIMO systems. In IEEE 20th international symposium on personal, indoor and mobile radio communications (pp. 3193–3197).

  39. Bi, W., Sun, F., Wang, C., & Lan, P. (2011). Transmit antenna selection and receive user scheduling for downlink of MISO systems. In 7th international conference on wireless communications, networking and mobile computing (pp. 1–4).

  40. Li, P. H., & Tsai, S. H. (2012). Performance analysis and algorithm designs for transmit antenna selection in linearly precoded multi-user MIMO systems. IEEE Transactions on Vehicular Technology,61(4), 1698–1708.

    Article  Google Scholar 

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Authors and Affiliations

  1. Chitkara University School of Engineering and Technology, Chitkara University, Solan, Himachal Pradesh, India

    Ashu Taneja

  2. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Nitin Saluja

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  1. Ashu Taneja
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  2. Nitin Saluja
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Correspondence to Ashu Taneja.

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Taneja, A., Saluja, N. Linear Precoding with User and Transmit Antenna Selection. Wireless Pers Commun 109, 1631–1644 (2019). https://doi.org/10.1007/s11277-019-06642-1

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  • DOI: https://doi.org/10.1007/s11277-019-06642-1

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