Mobility state estimation in LTE

Estimating mobile user speed is a problematic issue which has significant impacts to radio resource management and also to the mobility management of Long Term Evolution (LTE) networks. This paper introduces two algorithms that can estimate the speed of mobile user equipments (UE), with low computational requirement, and without modification of neither current user equipment nor 3GPP standard protocol. The proposed methods rely on uplink (UL) sounding reference signal (SRS) power measurements performed at the eNodeB (eNB) and remain efficient with large sampling period (e.g., 40 ms or beyond). We evaluate the effectiveness of our algorithms using realistic LTE system data provided by the eNB Layer1 team of Alcatel-Lucent. Results show that the classification of UE's speed required by LTE can be achieved with high accuracy. In addition, they have minimal impact to the central processing unit (CPU) and the memory of eNB modem. We see that they are very practical to today's LTE networks and would allow a continuous and real-time UE speed estimation.

[1]  Optimizing stored video delivery for mobile networks: The value of knowing the future , 2013, 2013 Proceedings IEEE INFOCOM.

[2]  Yahong Rosa Zheng,et al.  Mobile speed estimation for broadband wireless communications over Rician fading channels , 2009, IEEE Transactions on Wireless Communications.

[3]  Jay Weitzen,et al.  Measurement of angular and distance correlation properties of log-normal shadowing at 1900 MHz and its application to design of PCS systems , 2002, IEEE Trans. Veh. Technol..

[4]  Ali Abdi,et al.  Mobile speed estimation using diversity combining in fading channels , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[5]  Preben E. Mogensen,et al.  Experimental analysis of the joint statistical properties of azimuth spread, delay spread, and shadow fading , 2002, IEEE J. Sel. Areas Commun..

[6]  M. Marsan,et al.  Shadowing variability in an urban land mobile environment at 900 MHz , 1990 .

[7]  Geoffrey Ye Li,et al.  User Classification and Scheduling in LTE Downlink Systems with Heterogeneous User Mobilities , 2013, IEEE Transactions on Wireless Communications.

[8]  Sandeep Chennakeshu,et al.  Doppler spread estimation for wireless mobile radio systems , 2000, 2000 IEEE Wireless Communications and Networking Conference. Conference Record (Cat. No.00TH8540).

[9]  H. Suzuki,et al.  A Statistical Model for Urban Radio Propogation , 1977, IEEE Trans. Commun..

[10]  Yongbin Wei,et al.  A survey on 3GPP heterogeneous networks , 2011, IEEE Wireless Communications.

[11]  Sarhan M. Musa,et al.  An Analytical Approach for Mobility Load Balancing in Wireless Networks , 2011, J. Comput. Inf. Technol..

[12]  Jack M. Holtzman,et al.  Adaptive averaging methodology for handoffs in cellular systems , 1995 .

[13]  Katharina Burger,et al.  Random Data Analysis And Measurement Procedures , 2016 .

[14]  Olav Tirkkonen,et al.  Energy-Efficient Flexible Inter-Frequency Scanning Mechanism for Enhanced Small Cell Discovery , 2013, 2013 IEEE 77th Vehicular Technology Conference (VTC Spring).