AoI-Aware Optimization of Sub-Flow Scheduling for Multi-Path Transport Layer in Dual-Connectivity-Based Cellular Networks

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Miryeganeh Langeroudi Seyyed Hamidreza; Sheikhi Marzieh; Hakami Vesal

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Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2023 | Issue:9

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Title

AoI-Aware Optimization of Sub-Flow Scheduling for Multi-Path Transport Layer in Dual-Connectivity-Based Cellular Networks

Author(s)

Miryeganeh Langeroudi Seyyed Hamidreza | Sheikhi Marzieh | Hakami Vesal

Keywords

Traffic Scheduling

Internet of Things

Multi-Path Transport Layer

Dual-Connectivity

Age of Information

Reinforcement Learning

Abstract

Emerging applications (e. g., Internet of Things) in new generation wireless networks have strict information freshness requirements. Recently, Age of Information (AoI) has been introduced as a new QoS metric which differs from conventional measures such as delay and throughput. AoI is defined as the elapsed time since the generation of the last received packet in the destination. Optimal configuration of the transport and physical layer protocols is key to AoI minimization. In this paper, we study the problem of scheduling multi-path TCP (MPTCP) sub-flows over a Dual-Connectivity-based physical transmission medium based on LTE and mmWave technologies. The synergy of a Multi-Path Transport Layer and a multi-connectivity-based physical layer gives rise to an efficient communication setup for AoI minimization. In order to optimize the scheduling of traffic sub-flows over the two available paths, we propose a model-free optimization algorithm using Reinforcement Learning. We aim at minimizing the long-run mean AoI for the data packets received by the destination. Simulation results showcase the superiority of the proposed algorithm in comparison with the default MPTCP scheduling algorithms, i. e., round-robin and lowest RTT.

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APA: Copy

Miryeganeh Langeroudi, Seyyed Hamidreza, Sheikhi, Marzieh, & Hakami, Vesal. (). . . SID. https://sid.ir/paper/1047260/en

Vancouver: Copy

Miryeganeh Langeroudi Seyyed Hamidreza, Sheikhi Marzieh, Hakami Vesal. . . Available from: https://sid.ir/paper/1047260/en

IEEE: Copy

Seyyed Hamidreza Miryeganeh Langeroudi, Marzieh Sheikhi, and Vesal Hakami, “,” presented at the . , [Online]. Available: https://sid.ir/paper/1047260/en

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