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Alleviating cellular network congestion caused by traffic lights Hind ZAARAOUI, Zwi ALTMAN, Tania JIMENEZ, Eitan ALTMAN
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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
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interne Orange3 I. Definition of the problem Macro cell Small cell
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interne Orange4 II. Mobility model Overtaking is excluded in the simulation: The algorithm used in the simulation is therefore:
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interne Orange5 III. Radio modelling: instantaneous cell load definition
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interne Orange6 III. Radio modelling: network performance indicators
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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)
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interne Orange8 IV. Resource allocation small-macro cells (2/2)
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interne Orange9 Network and traffic characteristics
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interne Orange10 V. Simulation and results (1/2) Loads comparison MO MFqS Fq S Reuse
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interne Orange11 V. Simulation and results (1/2) Mean user throughput in time and mean file transfer time comparison
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interne Orange12 Conclusion and ongoing work
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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 = ?
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interne Orange14 Scheduling in presence of mobility (2/2)
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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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