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Joint MMSE transceiver design for downlink heterogeneous network

Abstract

In this paper, we propose a minimum mean square error (MMSE) based transceiver design scheme for a downlink multiple-input multiple-output two-tier heterogeneous network with general linear equality per-cell power constraints. Three practical channel models with both perfect and imperfect channel state information are used in simulations. In each channel model, we consider two system configurations, two data transmission schemes and two cellular cooperation scenarios. Our study shows that the proposed MMSE scheme is more flexible than interference alignment (IA) based scheme. For the cases where the IA-type scheme is applicable, the proposed scheme generally outperforms IA-type scheme in terms of average sum rate and bit error rate, but is computationally more complex than the IA-type scheme.

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References

  1. 1.

    Abdelnasser, A., Hossain, E., & Kim, D. I. (2014). Clustering and resource allocation for dense femtocells in a two-tier cellular OFDMA network. IEEE Transactions on Wireless Communications, 13(3), 1628–1641.

  2. 2.

    Hwang, I., Song, B., & Soliman, S. (2013). A holistic view on hyper-dense heterogeneous and small cell networks. IEEE Communications Magazine, 51(6), 20–27.

  3. 3.

    Ghosh, A., et al. (2012). “Heterogeneous cellular networks: From theory to practice. IEEE Communications Magazine, 50(6), 54–64.

  4. 4.

    Cisco White paper. (2017). Cisco visual networking index: Global mobile data traffic forecast update, 20162021 white paper (pp. 1–35).

  5. 5.

    Wu, W., Yang, Q., Gong, P., et al. (2016). Energy-efficient resource optimization for OFDMA-based multi-homing heterogeneous wireless networks. IEEE Transactions on Signal Processing, 64(22), 5901–5913.

  6. 6.

    Saquib, N., Hossain, E., Le, L. B., & Kim, D. I. (2012). Interference management in OFDMA femtocell networks: Issues and approaches. IEEE Wireless Communications, 19(3), 86–95.

  7. 7.

    Sharma, S., Chatzinotas, S., & Ottersten, B. (2013). Interference alignment for spectral coexistence of heterogeneous networks. EURASIP Journal on Wireless Communications and Networking, 2013(1), 1–14.

  8. 8.

    Guler, B., & Yener, A. (2014). Selective interference alignment for MIMO cognitive femtocell networks. IEEE Journal on Selected Areas in Communications, 32(3), 439–450.

  9. 9.

    Men, H., Zhao, N., Jin, M., & Kim, J. M. (2015). Optimal transceiver design for interference alignment based cognitive radio networks. IEEE Communications Letters, 19(8), 1442–1445.

  10. 10.

    Ben Halima, S., & Saadani, A. (2012). Joint clustering and interference alignment for overloaded femtocell networks. In Proceedings of IEEE wireless communications and networking conference (WCNC) (pp. 1229–1233), Shanghai.

  11. 11.

    Pantisano, F., Bennis, M., Saad, W., et al. (2013). Interference alignment for cooperative femtocell networks: A game-theoretic approach. IEEE Transactions on Mobile Computing, 12(99), 2233–2246.

  12. 12.

    Shin, W., Noh, W., Jang, K., & Choi, H. H. (2012). Hierarchical interference alignment for downlink heterogeneous networks. IEEE Transactions on Wireless Communications, 11(12), 4549–4559.

  13. 13.

    Vu, T. T., Kha, H. H., Muta, O., et al. (2017). Energy-efficient interference mitigation with hierarchical partial coordination for MIMO heterogeneous networks. IEICE Transactions on Communications, E100-B(6), 1023–1030.

  14. 14.

    Lu, E., Ma, T., & Lu, I. T. (2011). Interference alignment-like behaviors of MMSE designs for general multiuser MIMO systems. In Proceedings of IEEE global telecommunications conference (GLOBECOM), Houston, TX, USA.

  15. 15.

    Liu, G., Sheng, M., Wang, X., et al. (2015). Interference alignment for partially connected downlink MIMO heterogeneous networks. IEEE Transactions on Communications, 63(2), 551–564.

  16. 16.

    Shehata, M., Kurras, M., Hassan, K., et al. (2016). Hierarchical precoding in a realistic ultradense heterogeneous environment exceeding the degrees of freedom. International Journal of Antennas and Propagation. https://doi.org/10.1155/2016/4796474.

  17. 17.

    Peng, R., & Tian, Y. (2016). Inter-tier interference coordination in massive-MIMO system based on statistical channel information. In Proceedings of IEEE/CIC international conference on communications in China (ICCC workshops) (pp. 1–6), Chengdu.

  18. 18.

    Veetil, S. T., Kuchi, K., & Ganti, R. K. (2014). Spatial multiplexing in heterogeneous networks with MMSE receiver. In Proceedings of IEEE global communications conference (pp. 3684-3689), Austin, TX.

  19. 19.

    Cao, Y., Zhao, N., et al. (2018). Optimization or alignment: Secure primary transmission assisted by secondary networks. IEEE Journal on Selected Areas in Communications. https://doi.org/10.1109/JSAC.2018.2824360.

  20. 20.

    Zong, Z., Feng, H., et al. (2016). Optimal transceiver design for SWIPT in K-user MIMO interference channels. IEEE Transactions on Wireless Communications, 15(1), 430–445.

  21. 21.

    Li, J., Tai, I., & Lu, E. (2010). Robust MMSE transceiver designs for downlink MIMO systems with multicell cooperation. International Journal of Digital Multimedia Broadcasting. https://doi.org/10.1155/2010/815704.

  22. 22.

    Ding, M., & Blostein, S. D. (2009). MIMO minimum total MSE transceiver design with imperfect CSI at both ends. IEEE Transactions on Signal Processing, 57(3), 1141–1150.

  23. 23.

    GPP. (2017). Study on 3D channel model for LTE. 3rd Generation Partnership Project (3GPP), TR 36.873 V12.3.0.

  24. 24.

    Baum, D. S., Hansen, J., & Salo, J. (2005). An interim channel model for beyond-3G systems: Extending the 3GPP spatial channel model (SCM). In Proceedings of IEEE 61st vehicular technology conference (3132-3136).

  25. 25.

    Gomadam, K., Cadambe, V. R., & Jafar, S. A. (2011). A distributed numerical approach to interference alignment and applications to wireless interference networks. IEEE Transactions on Information Theory, 57(6), 3309–3322.

  26. 26.

    Strassen, V. (1969). Gaussian elimination is not optimal. Numerisch Mathematik, 13, 354–356.

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Correspondence to Hangsong Yan.

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Li, Z., Yan, H. & Lu, I. Joint MMSE transceiver design for downlink heterogeneous network. Wireless Netw 25, 3351–3364 (2019). https://doi.org/10.1007/s11276-018-1727-y

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Keywords

  • Heterogeneous networks
  • MIMO
  • MMSE
  • Interference alignment