Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management
This paper investigates resource management in device-to-device (D2D) networks coexisting with cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, me...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Vehicular Technology |
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| Online Access: | https://ieeexplore.ieee.org/document/10758763/ |
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| author | Parisa Parhizgar Mehdi Mahdavi Mohammad Reza Ahmadzadeh Melike Erol-Kantarci |
| author_facet | Parisa Parhizgar Mehdi Mahdavi Mohammad Reza Ahmadzadeh Melike Erol-Kantarci |
| author_sort | Parisa Parhizgar |
| collection | DOAJ |
| description | This paper investigates resource management in device-to-device (D2D) networks coexisting with cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network's energy needs, we formulate an online optimization problem. This formulation considers factors such as the quality of service (QoS) for both CUEs and D2Ds, available power, information freshness, and environmental sensing requirements. Due to the mixed-integer nonlinear nature and online characteristics of the problem, we propose a deep reinforcement learning (DRL) approach to solve it effectively. Numerical results show that the proposed joint scheduling and resource management strategy, utilizing the soft actor-critic (SAC) algorithm, reduces the average AoI by 20% compared to other baseline methods. |
| format | Article |
| id | doaj-art-6bb8802852ba411eaa84f7fd2a106e95 |
| institution | OA Journals |
| issn | 2644-1330 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Vehicular Technology |
| spelling | doaj-art-6bb8802852ba411eaa84f7fd2a106e952025-08-20T01:55:46ZengIEEEIEEE Open Journal of Vehicular Technology2644-13302025-01-016526710.1109/OJVT.2024.350280310758763Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource ManagementParisa Parhizgar0https://orcid.org/0009-0002-7799-0789Mehdi Mahdavi1https://orcid.org/0000-0003-4701-9377Mohammad Reza Ahmadzadeh2https://orcid.org/0000-0001-9558-5854Melike Erol-Kantarci3https://orcid.org/0000-0001-6787-8457Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, IranDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, IranDepartment of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, IranSchool of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, CanadaThis paper investigates resource management in device-to-device (D2D) networks coexisting with cellular user equipment (CUEs). We introduce a novel model for joint scheduling and resource management in D2D networks, taking into account environmental constraints. To preserve information freshness, measured by minimizing the average age of information (AoI), and to effectively utilize energy harvesting (EH) technology to satisfy the network's energy needs, we formulate an online optimization problem. This formulation considers factors such as the quality of service (QoS) for both CUEs and D2Ds, available power, information freshness, and environmental sensing requirements. Due to the mixed-integer nonlinear nature and online characteristics of the problem, we propose a deep reinforcement learning (DRL) approach to solve it effectively. Numerical results show that the proposed joint scheduling and resource management strategy, utilizing the soft actor-critic (SAC) algorithm, reduces the average AoI by 20% compared to other baseline methods.https://ieeexplore.ieee.org/document/10758763/Age of informationD2D communicationdeep reinforcement learningenergy harvestingresource allocationscheduling |
| spellingShingle | Parisa Parhizgar Mehdi Mahdavi Mohammad Reza Ahmadzadeh Melike Erol-Kantarci Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management IEEE Open Journal of Vehicular Technology Age of information D2D communication deep reinforcement learning energy harvesting resource allocation scheduling |
| title | Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management |
| title_full | Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management |
| title_fullStr | Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management |
| title_full_unstemmed | Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management |
| title_short | Enhancing Information Freshness and Energy Efficiency in D2D Networks Through DRL-Based Scheduling and Resource Management |
| title_sort | enhancing information freshness and energy efficiency in d2d networks through drl based scheduling and resource management |
| topic | Age of information D2D communication deep reinforcement learning energy harvesting resource allocation scheduling |
| url | https://ieeexplore.ieee.org/document/10758763/ |
| work_keys_str_mv | AT parisaparhizgar enhancinginformationfreshnessandenergyefficiencyind2dnetworksthroughdrlbasedschedulingandresourcemanagement AT mehdimahdavi enhancinginformationfreshnessandenergyefficiencyind2dnetworksthroughdrlbasedschedulingandresourcemanagement AT mohammadrezaahmadzadeh enhancinginformationfreshnessandenergyefficiencyind2dnetworksthroughdrlbasedschedulingandresourcemanagement AT melikeerolkantarci enhancinginformationfreshnessandenergyefficiencyind2dnetworksthroughdrlbasedschedulingandresourcemanagement |