DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets

Abdulmalik Alwarafy; Bekir Sait Ciftler; Mohamed Abdallah; Mounir Hamdi; Naofal Al-Dhahir

doi:10.1109/LWC.2022.3177250

DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets

Abdulmalik Alwarafy, Bekir Sait Ciftler, Mohamed Abdallah, Mounir Hamdi, Naofal Al-Dhahir

Research output: Contribution to journal › Article › peer-review

7 Citations (Scopus)

Abstract

Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNets. The problem is formulated as mixed-integer non-linear programming, whose objective is to cost-effectively maximize the network constrained sum-rate. Due to the high complexity of the problem, we propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve it. Simulation results show that our proposed scheme efficiently learns the optimal policy and enhances the network sum-rate by 80.95% compared to key benchmarks.

Original language	English
Pages (from-to)	1503-1507
Number of pages	5
Journal	IEEE Wireless Communications Letters
Volume	11
Issue number	7
DOIs	https://doi.org/10.1109/LWC.2022.3177250
Publication status	Published - Jul 1 2022
Externally published	Yes

Keywords

bandwidth
deep reinforcement learning
heterogeneous networks
power
RAT association

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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10.1109/LWC.2022.3177250

Cite this

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title = "DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets",

abstract = "Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNets. The problem is formulated as mixed-integer non-linear programming, whose objective is to cost-effectively maximize the network constrained sum-rate. Due to the high complexity of the problem, we propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve it. Simulation results show that our proposed scheme efficiently learns the optimal policy and enhances the network sum-rate by 80.95% compared to key benchmarks.",

keywords = "bandwidth, deep reinforcement learning, heterogeneous networks, power, RAT association",

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T1 - DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets

AU - Alwarafy, Abdulmalik

AU - Ciftler, Bekir Sait

AU - Abdallah, Mohamed

AU - Hamdi, Mounir

AU - Al-Dhahir, Naofal

PY - 2022/7/1

Y1 - 2022/7/1

N2 - Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNets. The problem is formulated as mixed-integer non-linear programming, whose objective is to cost-effectively maximize the network constrained sum-rate. Due to the high complexity of the problem, we propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve it. Simulation results show that our proposed scheme efficiently learns the optimal policy and enhances the network sum-rate by 80.95% compared to key benchmarks.

AB - Multi-radio access technologies (RATs) networks, where various heterogeneous networks (HetNets) coexist, are in service nowadays and considered a main enabling technology for future networks. In such networks, managing radio resources is challenge. In this letter, we address the problem of RATs-edge devices (EDs) association and joint power and bandwidth allocation in multi-RAT multi-homing HetNets. The problem is formulated as mixed-integer non-linear programming, whose objective is to cost-effectively maximize the network constrained sum-rate. Due to the high complexity of the problem, we propose a multi-agent deep reinforcement learning (DRL)-based scheme to solve it. Simulation results show that our proposed scheme efficiently learns the optimal policy and enhances the network sum-rate by 80.95% compared to key benchmarks.

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KW - deep reinforcement learning

KW - heterogeneous networks

KW - power

KW - RAT association

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DRL-Based Joint RAT Association, Power and Bandwidth Optimization for Future HetNets

Abstract

Keywords

ASJC Scopus subject areas

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