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Preprints, Working Papers, ... Year : 2020

Cross-layer Loss Discrimination Algorithms for MEC in 4G networks


Traditional loss-based Congestion Control Algorithms (CCAs) suffer from performance issues over wireless networks mostly because they fail to distinguish wireless random losses from congestion losses. Different loss discrimination algorithms have been proposed to tackle this issue but they are not efficient for 4G networks since they do not consider the impact of various link layer mechanisms such as adaptive modulation and coding and retransmission techniques on congestion in LTE Radio Access Networks (RANs). We propose MELD (MEC-based Edge Loss Discrimination), a novel server-side loss discrimination mechanism that leverages recent advancements in Multi-access Edge Computing (MEC) services to discriminate packet losses based on real-time RAN statistics. Our approach collects the relevant radio information via MEC's Radio Network Information Service and uses it to correctly distinguish random losses from congestion losses. Our experimental study made with the QUIC transport protocol shows over 80% higher goodput when MELD is used with New Reno and 8% higher goodput when used with Cubic.


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Dates and versions

hal-03001893 , version 1 (12-11-2020)


  • HAL Id : hal-03001893 , version 1


Mamoutou Diarra, Thierry Turletti, Walid Dabbous, Amine Ismail. Cross-layer Loss Discrimination Algorithms for MEC in 4G networks. 2020. ⟨hal-03001893⟩
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