JOURNAL BROWSE
Search
Advanced SearchSearch Tips
Simplified Near Optimal Downlink Beamforming Schemes in Multi-Cell Environment
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Simplified Near Optimal Downlink Beamforming Schemes in Multi-Cell Environment
Yang, Jang-Hoon; Kim, Dong-Ku;
  PDF(new window)
 Abstract
Despite enormous performance gain with multi-antenna transmission in the single cell environment, its gain diminishes out in the multi-cell environment due to interference. It is also very hard to solve the efficient downlink beamforming with low complexity in multi-cell environment. First, this paper shows that the asymptotically sum rate optimal downlink beamformings at low and high SNR are maximum ratio transmit (MRT) and zero forcing (ZF) beamforming in the multi-cell system, respectively. Secondly, exploiting the asymptotically optimal downlink beamforming, we develop simple two types of near optimal downlink beamforming schemes having the form of minimum mean squared error (MMSE) beamforming obtained from the dual uplink problem. For each type, three different subclasses are also considered depending on the computational complexity. The simulation results show that the proposed near optimum algorithms provide the trade-off between the complexity and the performance.
 Keywords
MIMO;multi-cell;beamforming;duality;MISO;sum rate maximization;
 Language
English
 Cited by
 References
1.
E. Telatar, "Capacity of multi-atenna Gaussian channels," AT\&T-Bell Technical Memorandum, 1995..

2.
S. Catreux, P. F. Driessen, and L. J. Greenstein, "`Simulation results for an interference-limited multiple-input multiple- output cellular system," IEEE Commun. Lett., vol.4, pp.334-336, Nov. 2000. crossref(new window)

3.
H. Zhang and H. Dai, "Cochannel Interference Mitigation and Cooperative Processing in Downlink Multicell Multiuser MIMO Networks," European Journal on Wireless Communications and Networking, 4th quarter 2004.

4.
S. A. Jafar, G. Foschini, A. Goldsmith, PhantomNet: Exploring optimal multicellular multiple antenna systems, EURASIP Journal on Applied Signal Processing, Special Issue on MIMO Communications and Signal Processing, No.5, pp. 591-605, May 2004.

5.
J. G. Andrews, W. Choi, and R. W. Heath, Jr., ``Overcoming Interference in Spatial Multiplexing MIMO Cellular Networks,'' IEEE Wireless Comm. Mag. vol. 14, no. 6, pp.95-104, Dec. 2007. crossref(new window)

6.
Ng B., Evans J, and Hanly S, "Distributed Downlink Beamforming in Cellular Networks, '' IEEE ISIT, France, June. 2007.

7.
Y. Wu, J. Zhang, M. Xu, S. Zhou, and X. Xu, "Multiuser MIMO downlink precoder design based on the maximal SJNR criterion," in Proc. IEEE Globecom, vol. 5, pp.2694-2698, Nov. 2005.

8.
K. Lee, J. Ko, and Y. Lee, "Downlink Beamforming for Other-cell Interference Mitigation in Correlated MISO Channels," European Wireless, France, Apr. 2007.

9.
T. Ren, and R. J. La, "Downlink Beamforming Algorithms with Inter-Cell Interference in Cellular Networks," IEEE Trans. Wireless Comm. vol.5, pp.2814-2822, Oct. 2006 crossref(new window)

10.
J. Yang and D. Kim, "Multi-cell Uplink/Downlink Beamforming Throughput Duality based on Lagrangian Duality With Per-Base Station Power Constraint," IEEE Comm. Lett, vol. 12, no. 4, pp. 277-279, Apr. 2008. crossref(new window)

11.
R. A. Monzingo and T. W. Miller, Introduction to Adaptive Arrays. New York: Wiley, 1980.

12.
Gascuel, and G. Caraux, ``Bounds on expectations of order statistics via extremal dependences," Statistics and Probability Letters, vol. 15, pp. 143-148, Sept. 1992. crossref(new window)