Scheduling Exploiting Frequency and Multi-User Diversity in LTE Downlink Systems with Heterogeneous Mobilities

Authors:

Garima Nagpal (Manav Rachna College of Engineering, Faridabad)

Anand Singh Rajpoot (Manav Rachna College of Engineering, Faridabad)

Keywords: Resource allocation, LTE downlink, Scheduling, mobility, frequency diversity, multi-user diversity, MIMO, user classification.

Abstract:

Long-term evolution (LTE) represents an emerging and promising technology for providing broadband ubiquitous Internet access. LTE systems involve the allocation of resources in a manner to benefit the user by providing high data rate to the users. In resource allocation there is a major role of scheduling which has become an essential component for high- speed wireless data systems. In LTE systems, frequency diversity scheduling benefits high mobility users while frequency selective scheduling or multiuser scheduling benefits low mobility users. Scheduling exploiting frequency diversity and selectivity is desired to benefit both low and high mobility users simultaneously. We first propose a user mobility classification algorithm to identify low and high mobility users , robust to different channel delay profiles for SISO systems, then extend it to MIMO systems. A low complexity scheduling algorithm is developed exploiting both frequency selectivity and diversity for low and high mobility users simultaneously. The proposed user classification algorithm is robust to different CDPs and the proposed scheduling algorithm is effective. In this paper we will discuss about various user classification algorithms and scheduling algorithms to overcome the constraints of MCS and to fulfil the requirement of Quality of Services (QoS). We’ll also analyze the throughput achieved by the user selected subband feedback scheme of LTE.

References:

[1] J.P. Niu, D.W. Lee, X.F. Ren, G.Y. Li, and T. Su, “Scheduling exploiting frequency and multi-user diversity in LTE downlink systems,” IEEE Trans. Wireless Commun., vol. 12, no. 4, pp. 1843–1849, 2013.

[2] F. Capozzi, G. Piro, L. Grieco, G. Boggia, and P. Camarda, “Downlink packet scheduling in lte cellular networks: Key design issues and a survey”, Communications Surveys Tutorials, IEEE, vol. 15, no. 2, pp. 678_700, 2013.

[3] Jinping Niu, Daewon Lee, Tao Su, Geoffrey Y. Li, Fellow, IEEE, and Xiaofeng Ren, “User Classification and Scheduling in LTE Downlink Systems with Heterogeneous User Mobilities”, IEEE Transactions On Wireless Communications, Vol. 12, No. 12, December 2013

[4] J. Fan, Q. Yin, G. Y. Li, B. Peng, and X. Zhu, “Adaptive block level resource allocation in OFDMA networks,” IEEE Trans. Wireless Commun., vol. 10, pp. 3966–3972, 2011.

[5] R. Kwan, C. Leung, and J. Zhang, “Multiuser scheduling on the downlink of an LTE cellular system,” Research Lett. Commun., vol. 2008, pp. 1–3, Jan. 2008.

[6] H. Kim and Y. Han, “A proportional fair scheduling for multicarrier transmission systems,” IEEE Commun. Lett., vol. 9, no. 3, pp. 210–212, Mar. 2005.

[7] S.N. Donthi and N. B. Mehta, “Joint performance analysis of channel quality indicator feedback schemes and frequency-domain scheduling for LTE,” IEEE Trans. Veh. Technol., vol. 60, no. 7, pp. 3096–3109, Sep. 2011.

[8] Sushruth N. Denthi and Neelesh B. Mehta, “An accurate model for EESM and its application to analysis of CQI feedback schemes and scheduling in LTE,” IEEE Trans. Commun., vol. 10, no. 10, pp. 3436–3448, Oct. 2011.

[9] S. J. Lee, “Trade-off between frequency diversity gain and frequency selective scheduling gain in OFDMA systems with spatial diversity,” IEEE Commun. Lett., vol. 11, no. 6, pp. 507–509, June 2007.

[10] A. Mills, D. Lister, M. D. Vos, and Y. Ji, “The impact of MS velocity on the performance of frequency selective scheduling in IEEE 802.16e Mobile WiMAX,” in Proc. 2010 IEEE Consumer Commun. Netw. Conf., pp. 1–5.

[11] J. Capka and R. Boutaba, “Mobility prediction in wireless networks using neural networks,” Manage. Multimedia Netw. Services, pp. 237– 246, 2004.

[12] Y.R. Zhen and C. Xiao, “Mobile speed estimation for broadband wireless communications,” in Proc. 2007 IEEE Wireless Commmun. Netw. Conf., pp. 2420–2425.

[13] J. Chan and A. Seneviratne, “A practical user mobility prediction algorithm for supporting adaptive QoS in wireless networks,” in Proc. 1999 IEEE Int. Conf. Netw., pp. 104–111.

[14] R. Kwan, C. Leung, and J. Zhang, “Proportional fair multiuser scheduling in LTE,” IEEE Signal Process. Lett., vol. 16, no. 6, pp. 461–464, June 2009.

[15] TS36.213 Evolved Universal Terrestrial Radio Access (E[1]UTRA); Physical Layer Procedures, 3GPP Std., Rev. 8.8.0, Sep. 2009.

[16] J.C. Fan, Q. Yin, G.Y. Li, B. Peng, and X. Zhu, “MCS selection for throughput improvement in downlink LTE systems,” in Proc. 2011 IEEE Int. Conf. Comput. Commun. Netw., pp. 1–5.

[17] F. Khan, LTE for 4G Mobile Broadband: Air Interface Technologies and Performance. Cambridge University Press, 2009.

[18] K. Pedersen, G. Monghal, I. Kovacs, T. Kolding, A. Pokhariyal, F. Frederiksen, and P. Mogensen, “Frequency domain scheduling for OFDMA with limited and noisy channel feedback,” in Proc. 2007 IEEE Veh. Technol. Conf., pp. 1792–1796.

[19] International Telecommunication Union, “Guidelines for evaluation of radio interface technologies for IMT[1]Advanced,” ITU-R, Rep. M.2135- 1, 2010.

[20] TS36.101 Evolved Universal Terrestrial Radio Access (E[1]UTRA); User Equipment (UE) Radio Transmission and Reception, 3GPP Std., Rev. 11.0.0, Mar. 2012.

[21] T. Rappaport, Wireless Communications: Principles and Practice. Prentice Hall, 2002.

[22] M. Assaad, “Reduction of the feedback delay impact on the performance of scheduling in OFDMA systems,” in Proc. 2009 IEEE Veh. Technol. Conf., pp. 1–4.