DOI QR코드

DOI QR Code

Reducing Feedback Overhead in Opportunistic Scheduling of Wireless Networks Exploiting Overhearing

  • Received : 2011.12.10
  • Accepted : 2012.01.27
  • Published : 2012.02.28

Abstract

We propose a scheme to reduce the overhead associated with channel state information (CSI) feedback required for opportunistic scheduling in wireless access networks. We study the case where CSI is partially overheard by mobiles and thus one can suppress transmitting CSI reports for time varying channels of inferior quality. We model the mechanism of feedback suppression as a Bayesian network, and show that the problem of minimizing the average feedback overhead is NP-hard. To deal with hardness of the problem we identify a class of feedback suppression structures which allow efficient computation of the cost. Leveraging such structures we propose an algorithm which not only captures the essence of seemingly complex overhearing relations among mobiles, but also provides a simple estimate of the cost incurred by a suppression structure. Simulation results are provided to demonstrate the improvements offered by the proposed scheme, e.g., a savings of 63-83% depending on the network size.

Keywords

Acknowledgement

Supported by : KEIT

References

  1. F. Jensen and T. Nielsen, "Bayesian Networks and Decision Graphs," in Journal of Springer, 2007.
  2. S. Sanayei, A. Nosratinia and N. Aldhahir, "Opportunistic downlink transmission with limited feedback," in Proc. of IEEE Transactions on Information Theory, 2004.
  3. D.J. Love, R.W. Heath Jr, V.K.N. Lau, D. Gesbert, B.D. Rao and M. Andrews, "An overview of limited feedback in wireless communication systems," IEEE Journal on Selected Areas in Communications, vol.26, no.8, pp.1341-1365, 2008.
  4. R. Agarwal, R. Vannithamby and J. M. Cioffi, "Optimal allocation of feedback bits for downlink OFDMA systems," in Proc. of IEEE International Symposium on Information Theory, pp. 1686-1690, Jul. 2008.
  5. X. Qin and R. Berry, "Opportunistic splitting algorithms for wireless networks with heterogeneous users," in Proc. International Symposium on Modeling and Optimization in Mobile, Ad Hoc and Wireless Networks, pp. 1662-1672, Mar.2004.
  6. T. Tang and R. Heath, "Opportunistic feedback for downlink multiuser diversity," IEEE Communication Letters, vol.9, pp.948-950, Oct.2005. https://doi.org/10.1109/LCOMM.2005.10002
  7. S. Patil and G. de Veciana, "Reducing feedback for opportunistic scheduling in wireless systems," IEEE Trans. on Wireless Communications, vol.6, no.12, pp.4227-4232, Dec.2007.
  8. D. Park, H. Seo, H. Kwon and B. G. Lee, "A new wireless packet scheduling algorithm based on cdf of user tranmission rate," in Proc. IEEE Globecom, 2003.
  9. T. Bonald, "A score-based opportunistic scheduler for fading radio channels," in Proc. European Wireless, 2004.
  10. S. Patil and G. de Veciana, "Managing resources and quality of service in wireless systems exploiting opportunism," IEEE/ACM Transactions on Networking, vol.15, pp.1046-1058, Oct.2007.
  11. G. Cooper, "The computational complexity of probabilistic inference using Bayesian belief networks.," Artificial Intelligence, vol.42, no.2, pp.393-405, 1990.
  12. P. Billingsley, "Convergence of probability measures," John Wiley & Sons, 1968.
  13. T. Feo and M. Resende, "Greedy randomized adaptive search procedures," J. of Global Optimization, vol.6, no.2, pp.109-133, 1995. https://doi.org/10.1007/BF01096763
  14. C. Chow and C. Liu, "Approximating discrete probability distributions with dependence trees," IEEE transactions on Information Theory, vol.14, no.3, pp.462-467, 1968. https://doi.org/10.1109/TIT.1968.1054142
  15. P. Burgisser, M. Clausen and M. Shokrollahi, Algebraic complexity theory, Springer Verlag, 1997