Skip to main content
SpringerLink
Log in

Downlink user selection and resource allocation for semi-elastic flows in an OFDM cell

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

We are concerned with user selection and resource allocation in wireless networks for semi-elastic applications such as video conferencing. While many packet scheduling algorithms have been proposed for elastic applications, and many user selection algorithms have been proposed for inelastic applications, little is known about optimal user selection and resource allocation for semi-elastic applications in wireless networks. We consider user selection and allocation of downlink transmission power and subcarriers in an orthogonal frequency division multiplexing cellular system. We pose a utility maximization problem, but find that direct solution is computationally intractable. We first propose a method that makes joint decisions about user selection and resource allocation by transforming the utility function into a concave function so that convex optimization techniques can be used, resulting in a complexity polynomial in the number of users with a bounded duality gap. This method can be implemented if the network communicates a shadow price for power to power allocation modules, which in turn communicate shadow prices for rate to individual users. We then propose a method that makes separate decisions about user selection and resource allocation, resulting in a complexity linear in the number of users.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Notes

  1. A major advantage of OFDM is that each subcarrier can be considered as flat fading. Aspects of frequency selective fading are typically addressed at the physical layer.

  2. Many optimization methods may be used; below we propose a bisection method.

  3. Many optimization methods may be used; below we propose a subgradient method.

  4. Rayleigh channel with 6 paths with delays = [0,  0.2,  0.5,  1.6,  2.3,  5.0]*10−6 sec and fading = [1,  0.3,  0.6,   − 0.6,   − 0.8,   − 1]dB, generated using the Matlab routine rayleighchan.

  5. Power will scale linearly with I + δ2.

References

  1. Sandvine. (2011). Global Internet phenomena report. http://www.sandvine.com

  2. Cioffi J. M. (2003). Digital communication. EE379 course reader. Stanford: Stanford University.

  3. Liu, P., Zhang, P., Jordan, S., & Honig, M. (2004). Single-cell forward link power allocation using pricing in wireless networks. IEEE Transactions on Wireless Communications. 3(2), 533–543.

    Article  Google Scholar 

  4. Jang, J., & Lee, K. B. (2003). Transmit power adaptation for multiuser OFDM systems. IEEE Journal on Selected Areas in Communications. 21(2), 171–178.

    Article  Google Scholar 

  5. Shen, Z., Andrews, J., & Evans, B. (2005). Adaptive resource allocation in multiuser OFDM systems with proportional rate constraints. IEEE Transactions on Wireless Communications. 4(6), 2726–2737.

    Article  Google Scholar 

  6. Wong, C. Y., Cheng, R., Lataief, K., & Murch, R. (1999). Multiuser OFDM with adaptive subcarrier, bit, and power allocation. IEEE Journal on Selected Areas in Communications. 17(10), 1747–1758.

    Article  Google Scholar 

  7. Tsang, Y. M., & Cheng, R. S. (2004). Optimal resouce allocation in SDMA/MIMO/OFDM systems under QoS and power constraints. In Proc. WCNC, pp. 1595–1600.

  8. Gao, X., Nandagopal, T., & Bharghavan, V. (2001). Achieving application level fairness through utility-based wireless fair scheduling. In Proceedings of the GlobeCom, pp. 3257–3261.

  9. Feng, N., Mau, S. C., & Mandayam, N. (2004). Pricing and power control for joint network-centric and user-centric radio resource management. IEEE Transactions on Wireless Communications 52, 1547–1557.

    Article  Google Scholar 

  10. Song, G., & Li, Y. (2005). Utility-based resource allocation and scheduling in OFDM-based wireless broadband networks. IEEE Communications Magazine 43(12), 127–134.

    Article  MathSciNet  Google Scholar 

  11. Zhou, C., Honig, M., & Jordan, S. (2005). Utility-based power control for a two-cell CDMA data network. IEEE Transactions on Wireless Communications 4(6), 2764–2776.

    Article  Google Scholar 

  12. Yang, C., Wang, W., & Zhang, X. (2009). Multi-service transmission in multiuser cooperative networks. In Proceedings of the WCNC, pp. 1–5.

  13. Lee, J., Mazumdar, R., & Shroff, N. (2005). Downlink power allocation for multi-class wireless systems. IEEE/ACM Transactions on Networking 13(4), 854–867.

    Article  Google Scholar 

  14. Hande, P., Shengyu, Z., & Mung, C. (2007). Distributed rate allocation for inelastic flows. IIEEE/ACM Transactions on Networking 15(6), 1240–1253.

    Article  Google Scholar 

  15. Cheung, M. H., Mohsenian-Rad, A.-H., Wong, V., & Schober, R. (2010). Random access for elastic and inelastic traffic in WLANs. IEEE Transactions on Wireless Communications 9(6), 1861–1866.

    Article  Google Scholar 

  16. Abbas, G., Nagar, A. K., & Tawfik, H. (2011). On unified quality of service resource allocation scheme with fair and scalable traffic management for multiclass Internet services. IET Transaction on Communication 5(16), 2371–2385.

    Article  MathSciNet  Google Scholar 

  17. Jin, J., Sridharan, A., Krishnamachari, B., & Palaniswami, M. (2010). Handling inelastic traffic in wireless sensor networks. IEEE Journal on Selected Areas in Communications 28(7), 1105–1115.

    Article  Google Scholar 

  18. Song, G. C., & Li, Y. (2005). Cross-layer optimization for OFDM wireless networks-part I: Theoretical framework. IEEE Transactions on Wireless Communications 4(2), 614–624.

    Article  MathSciNet  Google Scholar 

  19. Zhou, C., Zhang, P., Honig, M., & Jordan, S. (2004). Two-cell power allocation for downlink CDMA. IEEE Transactions on Wireless Communications 3(6), 2256–2266.

    Article  Google Scholar 

  20. Ng, T. C.-Y., & Yu, W. (2007). Joint optimization of relay strategies and resource allocations in cooperative cellular networks. IEEE Journal on Selected Areas in Communications 25(2), 328–339.

    Article  Google Scholar 

  21. Boyd S., Vandenberghe L. (2004). Convex optimization. Cambridge: Cambridge University Press.

    MATH  Google Scholar 

  22. Chu, T. G., & Wang, L. (2009). Self-learning PD game with imperfect information on networks. In Proceedings of the CDC, pp. 6864–6869.

  23. Boyd, S., Xiao, Li., & Mutapcic, A. (2003). Subgradient methods. Lecture notes of EE392o. Stanford: Stanford University.

  24. ETSI. (1993). GSM specification 05.05 annex c.

Download references

Acknowledgments

This work has been supported by the National Science Foundation.

Author information

Authors and Affiliations

  1. University of California Irvine, Irvine, CA, USA

    Chao Yang & Scott Jordan

Authors
  1. Chao Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Scott Jordan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Scott Jordan.

Additional information

The preliminary version was presented at the 2011 IEEE Global Communications Conference (GLOBECOM 2011)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, C., Jordan, S. Downlink user selection and resource allocation for semi-elastic flows in an OFDM cell. Wireless Netw 19, 1407–1421 (2013). https://doi.org/10.1007/s11276-013-0541-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-013-0541-9

Keywords

Search

Navigation