Forecast scheduling for mobile users Hind ZAARAOUI, Zwi ALTMAN, Eitan ALTMAN and Tania JIMENEZ Idefix, November 3rd 2015

interne Orange2 Introduction Context:  Generate maps from measurements (SINR, received power) via drive tests Minimization of Drive Test (MDT)  Utilize maps to optimize the network Resource allocation (admission control, scheduling, …) network parameters Self-organizing network (SON) function (load balancing, HO optimization,…)

interne Orange3 A better scheduling in the context of mobility

interne Orange4 Context Normal scheduling t = 1 scheduling percentile relative to the mobile user = 50% Normal scheduling t = 2 scheduling percentile relative to the mobile user < 50% Dynamical scheduling t = 1 scheduling percentile relative to the mobile user > 50% Dynamical scheduling t = 2 scheduling percentile relative to the mobile user = ?

interne Orange5 What do we need? Data prevision

interne Orange6 How can we get it ? Drive-test MDT data collection Data rate/SINR prevision: GPS data: using signals from GPS satellites Base station delay measurements … Speed prevision: Deterministic trajectories: highways, Avenues,… GPS data: travels and directions provided by GPS for each user. Trajectory prevision: Interpolation for other locations

interne Orange7 Forecast scheduler model

interne Orange8 Forecast scheduling model and resolution method 1/3

interne Orange9 Forecast scheduling model and resolution method 2/3 Which is equivalent to :

interne Orange10 Forecast scheduling model and resolution method 3/3 Hence, If the data ratios for two times are not equal, the two users cannot be scheduled at the same time after.

interne Orange11 Resolution for the case of 2 users: existence of K Where: and

interne Orange12 Resolution for the case of 2 users: if K doesn’t exist Where

interne Orange13 Numerical results Mean user throughput for fixed user / mobile user (Mo) Gain forecast scheduler/ alpha-fair scheduler % 5 – 1025% %

interne Orange14 The resolution of the general case is quite difficult (or maybe impossible) analytically. Numerical simulations are therefore needed. However, using Optimization Application in Matlab doesn’t give a convergent solution for 20 users and 61 times (our example). A heuristic algorithm is used taking into account our analytical solution for the forecast scheduler for two specific users. Resolution with convex problem Resolution for the general case

interne Orange15 Two heuristic algorithms are used taking into account our analytical solution for the forecast scheduler for two specific users. Best users of two clustersTwo best Alpha-fair users Other solutions? -A time dynamical 2-clustering -…?

interne Orange16 Two scheduler algorithms for the general case 1/2 Two best Alpha-fair users Alpha-fair scheduler : choice of two best users Forecast scheduler: The best user

interne Orange17 Two scheduler algorithms for the general case 2/2 Best users of two clusters: group of mobile users and group of fixed users Best user in Mobile users cluster Best user in Fixed users cluster Best user with Forecast Scheduler Alpha-fair scheduler : best Mobile user Alpha-fair scheduler : best Fixed user

interne Orange18 Mean 1 Mean 5 Mean 15 2 clusters 2 best users Mean user throughput gain for the heuristic algo forecast scheduling compared to alpha-fair scheduler Nb of mobile users: 10 Nb of fixed users: 1 50

interne Orange19 Conclusion - Forecast scheduling gives a good improvement to network performance in the context of mobility, - Ongoing works:  Analytical and numerical solutions for the general case,  Application to Radio Environment Maps (REM): Drive-test  Beam focusing with forecast scheduling problem in the context of high speed mobility

interne Orange20 Beam focusing antenna array technology for non-stationary mobility pattern

Virtual Small Cell (VSC) Virtual Sectorization (ViS) VSC + SON Multilevel beamforming Multilevel beamforming for heterogeneous antenna system (same site) Mobility in the context of antenna array technology H. Zaaraoui at al, “Beam focusing antenna array technology for non-stationary mobility”, submitted to IEEE WCNC, 2016.

Thank you for your attention !