Alleviating cellular network congestion caused by traffic lights Hind ZAARAOUI, Zwi ALTMAN, Tania JIMENEZ, Eitan ALTMAN
interne Orange2 II. III. IV. V. Definition of the problem Mobility model Simulation and results Resource allocation small-macro cells Radio modelling I. VI.Conclusion and ongoing work
interne Orange3 I. Definition of the problem Macro cell Small cell
interne Orange4 II. Mobility model Overtaking is excluded in the simulation: The algorithm used in the simulation is therefore:
interne Orange5 III. Radio modelling: instantaneous cell load definition
interne Orange6 III. Radio modelling: network performance indicators
interne Orange7 IV. Resource allocation small-macro cells (1/2) Macro-cell only Full frequency reuse (macro & small cells) Dynamic frequency bandwidth splitting (macro & small) Mean optimal frequency bandwidth split (macro & small)
interne Orange8 IV. Resource allocation small-macro cells (2/2)
interne Orange9 Network and traffic characteristics
interne Orange10 V. Simulation and results (1/2) Loads comparison MO MFqS Fq S Reuse
interne Orange11 V. Simulation and results (1/2) Mean user throughput in time and mean file transfer time comparison
interne Orange12 Conclusion and ongoing work
interne Orange13 Scheduling in presence of mobility (1/2) 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 Orange14 Scheduling in presence of mobility (2/2)
Multilevel beamforming in mobility scenarios Context: Massive MIMO technology evolves rapidly towards antenna arrays with larger size, allowing to support multilevel beamforming Multilevel beamforming is based on hierarchical beam structure which reduces the scheduling complexity Objective: adapt multilevel beamforming to different mobility scenarios
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