Satellite-based ITS Data Offloading & Computation in 6G Networks: A Cooperative Multi-Agent Proximal Policy Optimization DRL with Attention Approach Article Swipe

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Sheikh Salman Hassan , Yu Min Park , Yan Kyaw Tun , Walid Saad , Zhu Han , Choong Seon Hong ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2212.05757

The proliferation of intelligent transportation systems (ITS) has led to increasing demand for diverse network applications. However, conventional terrestrial access networks (TANs) are inadequate in accommodating various applications for remote ITS nodes, i.e., airplanes and ships. In contrast, satellite access networks (SANs) offer supplementary support for TANs, in terms of coverage flexibility and availability. In this study, we propose a novel approach to ITS data offloading and computation services based on SANs. We use low-Earth orbit (LEO) and cube satellites (CubeSats) as independent mobile edge computing (MEC) servers that schedule the processing of data generated by ITS nodes. To optimize offloading task selection, computing, and bandwidth resource allocation for different satellite servers, we formulate a joint delay and rental price minimization problem that is mixed-integer non-linear programming (MINLP) and NP-hard. We propose a cooperative multi-agent proximal policy optimization (Co-MAPPO) deep reinforcement learning (DRL) approach with an attention mechanism to deal with intelligent offloading decisions. We also decompose the remaining subproblem into three independent subproblems for resource allocation and use convex optimization techniques to obtain their optimal closed-form analytical solutions. We conduct extensive simulations and compare our proposed approach to baselines, resulting in performance improvements of 9.9%, 5.2%, and 4.2%, respectively.

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Computer Science

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Reinforcement Learning

Edge Computing

Enhanced Data Rates For Gsm Evolution

Artificial Intelligence

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Computer science Computation offloading Distributed computing Server Schedule Bandwidth allocation Resource allocation Scheduling (production processes) Optimization problem Reinforcement learning Computer network Edge computing Mathematical optimization Bandwidth (computing) Enhanced Data Rates for GSM Evolution Artificial intelligence Algorithm Mathematics Operating system

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2212.05757
PDF: https://arxiv.org/pdf/2212.05757
OA Status: green
Cited By: 1
Related Works: 10
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DOI: https://doi.org/10.48550/arxiv.2212.05757

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Title: Satellite-based ITS Data Offloading & Computation in 6G Networks: A Cooperative Multi-Agent Proximal Policy Optimization DRL with Attention Approach

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Type: preprint

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Language: en

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Publication year: 2022

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Publication date: 2022-12-12

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Authors: Sheikh Salman Hassan, Yu Min Park, Yan Kyaw Tun, Walid Saad, Zhu Han, Choong Seon Hong

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Landing page: https://arxiv.org/abs/2212.05757

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PDF URL: https://arxiv.org/pdf/2212.05757

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Concepts: Computer science, Computation offloading, Distributed computing, Server, Schedule, Bandwidth allocation, Resource allocation, Scheduling (production processes), Optimization problem, Reinforcement learning, Computer network, Edge computing, Mathematical optimization, Bandwidth (computing), Enhanced Data Rates for GSM Evolution, Artificial intelligence, Algorithm, Mathematics, Operating system

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Cited by: 1

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abstract_inverted_index.attention	146
abstract_inverted_index.bandwidth	105
abstract_inverted_index.computing	85
abstract_inverted_index.contrast,	37
abstract_inverted_index.decompose	156
abstract_inverted_index.different	109
abstract_inverted_index.extensive	181
abstract_inverted_index.formulate	113
abstract_inverted_index.generated	94
abstract_inverted_index.low-Earth	74
abstract_inverted_index.mechanism	147
abstract_inverted_index.remaining	158
abstract_inverted_index.resulting	190
abstract_inverted_index.satellite	38, 110
abstract_inverted_index.(Co-MAPPO)	138
abstract_inverted_index.(CubeSats)	80
abstract_inverted_index.allocation	107, 166
abstract_inverted_index.analytical	177
abstract_inverted_index.baselines,	189
abstract_inverted_index.computing,	103
abstract_inverted_index.decisions.	153
abstract_inverted_index.inadequate	23
abstract_inverted_index.increasing	10
abstract_inverted_index.non-linear	125
abstract_inverted_index.offloading	65, 100, 152
abstract_inverted_index.processing	91
abstract_inverted_index.satellites	79
abstract_inverted_index.selection,	102
abstract_inverted_index.solutions.	178
abstract_inverted_index.subproblem	159
abstract_inverted_index.techniques	171
abstract_inverted_index.closed-form	176
abstract_inverted_index.computation	67
abstract_inverted_index.cooperative	133
abstract_inverted_index.flexibility	51
abstract_inverted_index.independent	82, 162
abstract_inverted_index.intelligent	3, 151
abstract_inverted_index.multi-agent	134
abstract_inverted_index.performance	192
abstract_inverted_index.programming	126
abstract_inverted_index.simulations	182
abstract_inverted_index.subproblems	163
abstract_inverted_index.terrestrial	18
abstract_inverted_index.applications	27
abstract_inverted_index.conventional	17
abstract_inverted_index.improvements	193
abstract_inverted_index.minimization	120
abstract_inverted_index.optimization	137, 170
abstract_inverted_index.accommodating	25
abstract_inverted_index.applications.	15
abstract_inverted_index.availability.	53
abstract_inverted_index.mixed-integer	124
abstract_inverted_index.proliferation	1
abstract_inverted_index.reinforcement	140
abstract_inverted_index.respectively.	199
abstract_inverted_index.supplementary	43
abstract_inverted_index.transportation	4
cited_by_percentile_year
countries_distinct_count	0
institutions_distinct_count	6
citation_normalized_percentile