Robust Interference Management via Linear Precoding and Linear/Non-Linear Equalization

详细信息    查看全文

• 作者：Seok-Hwan Park ; Ali M. Fouladgar ; Tariq Elkourdi…
• 关键词：Multi ; cell MIMO ; Linear precoding ; Sum ; rate maximization ; Robust optimization ; Bounded uncertainty ; Decision ; feedback equalization ; ADMM
• 刊名：The Journal of VLSI Signal Processing
• 出版年：2016
• 出版时间：May 2016
• 年：2016
• 卷：83
• 期：2
• 页码：133-149
• 全文大小：1,546 KB
• 参考文献：1.Andrews, J.G., Buzzi, S., Choi, W., Hanly, S.V., Lozano, A., Soong, A.C.K., & Zhang, J.C. (2014). What will 5G be? IEEE Journal on Selected Areas in Communications, 32(6), 1065–1082.CrossRef
  2.Huh, H., Papadopoulos, H.C., & Caire, G. (2010). Multiuser MISO transmitter optimization for intercell interference mitigation. IEEE Transactions on Signal Processing, 58(8), 4272–4285.MathSciNet CrossRef
  3.Stotz, D., & Bolcskei, H. Degrees of freedom in vector interference channels. arXiv:1210.​2259 .
  4.Weeraddana, P.C., Codreanu, M., Latva-aho, M., Ephremides, A., & Fischione, C. (2012). Weighted sum-rate maximization in wireless networks: A review. Foundations and Trends in Networking, 6(1–2), 1–163.MATH
  5.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.MathSciNet CrossRef
  6.Hong, M., Sun, R.-Y., Baligh, H., & Luo, Z.-Q. (2013). Joint base station clustering and beamformer design for partial coordinated transmission in heterogeneous networks. IEEE Journal on Selected Areas in Communications, 31(2), 226–240.CrossRef
  7.Jafar, S.A. (2011). Interference alignment: a new look at signal dimensions in a communication network. Foundations and Trends in Communications and Information Theory, 7(1), 1–136.MathSciNet CrossRef MATH
  8.Tajer, A., Prasad, N., & Wang, X. (2011). Robust linear precoder design for multi-cell downlink transmission. IEEE Transactions on Signal Processing, 59(1), 235–251.MathSciNet CrossRef
  9.Gharavol, E.A., & Larsson, E.G. (2013). The sign-definiteness lemma and its applications to robust transceiver optimization for multiuser MIMO systems. IEEE Transactions on Signal Processing, 61(2), 238–252.MathSciNet CrossRef
  10.Eldar, Y.C., & Merhav, N. (2004). A competitive minimax approach to robust estimation of random parameters. IEEE Transactions on Signal Processing, 52(7), 1931–1946.MathSciNet CrossRef
  11.Patcharamaneepakorn, P., Doufexi, A., & Armour, S. (2013). Leakage-based transceiver designs for MIMO interfering broadcast channels. In Proceedings IEEE wireless communications and signal processing (WCSP 2013), Hangzhou, China.
  12.Jose, J., Prasad, N., Khojastepour, M., & Rangarajan, S. (2011). On robust weighted-sum rate maximization in MIMO interference networks. In Proceedings of IEEE international conference on communications (ICC 2011), Kyoto, Japan.
  13.Huang, Y., Li, Q., Ma, W.-K., & Zhang, S. (2012). Robust multicast beamforming for spectrum sharing-based cognitive radios. IEEE Transactions on Signal Processing, 60(1), 527–533.MathSciNet CrossRef
  14.Bolcskei, H., & Thukral, I.J. (2009). Interference alignment with limited feedback. In Proceedings IEEE int. symp. inf. theory (ISIT 2009), Seoul, Korea.
  15.Krishnamachari, R.T., & Varanasi, M.K. (2013). Interference alignment under limited feedback for MIMO interference channels. IEEE Transactions on Signal Processing, 61(15), 3908–3917.MathSciNet CrossRef
  16.Shenouda, M.B., & Davidson, T.N. (2007). Tomlinson-Harashima precoding for broadcast channels with uncertainty. IEEE Journal on Selected Areas in Communications, 25(7), 1380–1389.CrossRef
  17.Ntranos, V., & Caire, G. (2012). A comparison of decoding techniques for interference alignment. In Proceedings IEEE international symposium on communications, control and signal processing (ISCCSP 2012), Rome, Italy.
  18.Sidiropoulos, N.D., Davidson, T.N., & Luo, Z.-Q. (2006). Transmit beamforming for physical-layer multicasting. IEEE Transactions on Signal Processing, 54(6), 2239–2251.CrossRef
  19.Khojastepour, M.A., Salehi-Golsefidi, A., & Rangarajan, S. (2011). Towards an optimal beamforming algorithm for physical layer multicasting. In Proceedings IEEE inf. theory workshop (ITW 2011), Paraty, Brazil.
  20.Zhu, H., Prasad, N., & Rangarajan, S. (2012). Precoder design for physical layer multicasting. IEEE Transactions on Signal Processing, 60(11), 5932–5947.MathSciNet CrossRef
  21.Xiang, Z., Tao, M., & Wang, X. (2013). Coordinated multicast beamforming in multicell networks. IEEE Transactions on Wireless Communications, 12(1), 12–21.CrossRef
  22.Sesia, S., Toufik, I., & Baker, M. (2011). LTE - The UMTS long term evolution: from theory to practice. Wiley.
  23.Torbatian, M., Najafi, H., & Damen, M.O. (2012). Asynchronous interference alignment. IEEE Transactions on Wireless Communications, 11(9), 3148–3157.CrossRef
  24.Christensen, S.S., Agarwal, R., Carvalho, E., & Cioffi, J. (2008). “Weighted sum-rate maximization using weighted MMSE for MIMO-BC beamforming design,”. IEEE Transactions on Wireless Communications, 7 (12), 4792–4799.CrossRef
  25.Shi, Q., Razaviyayn, M., Luo, Z.-Q., & He, C. (2011). An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel. IEEE Transactions on Signal Processing, 59 (9), 4331–4340.MathSciNet CrossRef
  26.Borwein, J., & Lewis, A (2006). Convex analysis and nonlinear optimization: theory and examples. Springer Verlag.
  27.Boyd, S., Parikh, N., Chu, E., Peleato, B., & Eckstein, J. (2011). Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning, 3(1), 1–122.CrossRef MATH
  28.Shenouda, M.B., & Davidson, T.N. (2008). A framework for designing MIMO systems with decision feedback equalization or Tomlinson-Harashima precoding. IEEE Journal on Selected Areas in Communications, 26(2), 401–411.CrossRef
  29.Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
  30.Hong, M., & Luo, Z.-Q. (2012). On the linear convergence of the alternating direction method of multipliers. arXiv:1208.​3922 .
  31.Boyd, S., Ghaoui, L.E., Feron, E., & Balakrishnan, V. (1994). Linear matrix inequalities in system and control theory. Philadelphia: SIAM Studies in Applied Mathematics.CrossRef MATH
  
• 作者单位：Seok-Hwan Park (1)
  Ali M. Fouladgar (2)
  Tariq Elkourdi (2)
  Osvaldo Simeone (2)
  Onur Sahin (3)
  Shlomo Shamai (Shitz) (4)
  
  1. Chonbuk National University, Jeonju-si, Jeonbuk, 561-756, Korea
  2. The Center for Wireless Communications and Signal Processing Research (CWCSPR) ECE Department, New Jersey Institute of Technology (NJIT), Newark, NJ, 07102, USA
  3. InterDigital Inc., Melville, NY, 11747, USA
  4. The Department of Electrical Engineering, Technion, Haifa, 32000, Israel
  
• 刊物类别：Engineering
• 刊物主题：Electrical Engineering
  Circuits and Systems
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Computer Systems Organization and Communication Networks
  Signal,Image and Speech Processing
  Mathematics of Computing
  
• 出版者：Springer New York
• ISSN：1939-8115

文摘

This work studies the robust design of linear precoding and linear/ non-linear equalization for multi-cell MIMO systems in the presence of imperfect channel state information (CSI). A worst-case design approach is adopted whereby the CSI error is assumed to lie within spherical sets of known radius. First, the optimal robust design of linear precoders is tackled for a MIMO interference broadcast channel (MIMO-IBC) with general unicast/multicast messages in each cell and operating over multiple time-frequency resources. This problem is formulated as the maximization of the worst-case sum-rate assuming optimal detection at the mobile stations (MSs). Then, symbol-by-symbol non-linear equalization at the MSs is considered. In this case, the problem of jointly optimizing linear precoding and decision-feedback (DF) equalization is investigated for a MIMO interference channel (MIMO-IC) with the goal of minimizing the worst-case sum-mean squared error (MSE). Both problems are addressed by proposing iterative algorithms with descent properties. The algorithms are also shown to be implementable in a distributed fashion on processors that have only local and partial CSI by means of the Alternating Direction Method of Multipliers (ADMM). From numerical results, it is shown that the proposed robust solutions significantly improve over conventional non-robust schemes in terms of sum-rate or symbol error rate. Moreover, it is seen that the proposed joint design of linear precoding and DF equalization outperforms existing separate solutions.