Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Beamforming Games with Quantized CSI in Two-user MISO ICs

두 유저 MISO 간섭 채널에서 불완전한 채널 정보에 기반한 빔포밍 게임

  • Lee, Jung Hoon (Department of Eletronics Engineering, Hankuk University of Foreign Studies) ;
  • Lee, Jin (Samsung Electronics) ;
  • Ryu, Jong Yeol (Department of Information and Communications Engineering, Gyeongsang National University)
  • Received : 2017.05.05
  • Accepted : 2017.05.22
  • Published : 2017.07.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we consider a beamforming game between the transmitters in a two-user multiple-input single-output interference channel using limited feedback and investigate how each transmitter is able to find a modified strategy from the quantized channel state information (CSI). In the beamforming game, each of the transmitters (i.e., a player) tries to maximize the achievable rate (i.e., a payoff function) via a proper beamforming strategy. In our case, each transmitter's beamforming strategy is represented by a linear combining factor between the maximum ratio transmission (MRT) and the zero forcing (ZF) beamforming vectors, which is the Pareto optimal achieving strategy. With the quantized CSI, the transmitters' strategies may not be valid because of the quantization errors. We propose a modified solution, which takes into account the effects of the quantization errors.

본 논문에서는 다중 안테나를 사용하는 두 개의 송신단이 각각 하나의 안테나를 가지고 있는 수신단을 서비스 할때 구성되는 두 유저 간섭 채널에서 불완전한 채널 정보를 사용하는 송신단들의 빔포밍 게임을 고려한다. 빔포밍 게임에서는 송신단(i.e., 플레이어)이 자신들의 전송률(i.e., 보상)을 높이기 위해서 경쟁을 하고, 빔포밍 기법을 위해서는 완전한 채널 상황에서 최적이라고 알려진 maximum ratio transmission (MRT) 기법과 zero forcing (ZF) 빔포밍 기법을 선형으로 결합하는 기법(i.e., 전략)을 사용한다. 우리가 제안하는 게임에서는 송신단이 불완전한 채널 정보를 사용하므로, 최적의 전략을 찾더라도 그 전략이 유효하지 않을 수 있다. 본 논문에서는 불완전한 채널 정보로부터 양자화 오차의 영향을 고려한 송신단의 효율적인 빔포밍 전략을 제안한다.

Keywords

References

  1. R. B. Myerson, Game Theory: Analysis of Conflict. Cambridge, MA: Harvard Univ. Press, 1991.
  2. E. G. Larsson and E. A. Jorswieck, "Competition versus cooperation on the MISO interference channel," IEEE Journal of Selected Areas on Communications, vol. 26, no. 7, pp. 1059-1069, Sep. 2008. https://doi.org/10.1109/JSAC.2008.080904
  3. Z. K. M. Ho and D. Gesbert, "Balancing egoism and altruism on interference channel: The MIMO case," in Proceeding of IEEE International Conference on Communications (ICC 2010), May 2010.
  4. Q. Cao, Y. Jing, and H. V. Zhao, "Power allocation in multi-user wireless relay networks through bargaining," IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 2870-2882, June 2013. https://doi.org/10.1109/TWC.2013.050313.121183
  5. A. Mukherjee and A. L. Swindlehurst, "Jamming games in the MIMO wiretap channel with an active eavesdropper," IEEE Transactions on Signal Processing, vol. 61, no. 1, pp. 82-91, Jan. 2013. https://doi.org/10.1109/TSP.2012.2222386
  6. J. H. Lee and W. Choi, "Optimal feedback rate sharing strategy in zeroforcing MIMO broadcast channels," IEEE Transactions on Wireless Communications, vol. 12, no. 6, pp. 3000-3011, June 2013. https://doi.org/10.1109/TWC.2013.050713.121410
  7. J. H. Lee and W. Choi, "Unified codebook design for vector channel quantization in MIMO broadcast channels," IEEE Transactions on Signal Processing, vol. 63, no. 10, pp. 2509- 2519, May 2015. https://doi.org/10.1109/TSP.2015.2414895
  8. E. A. Jorswieck, E. G. Larsson, and D. Danev, "Complete characterization of the Pareto boundary for the MISO interference channel," IEEE Transactions on Signal Processing, vol. 56, no. 10, pp. 5292-5296, Oct. 2008. https://doi.org/10.1109/TSP.2008.928095