Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions
As one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10477602/ |
| _version_ | 1797217385099296768 |
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| author | Mohammad Parvini Philipp Schulz Gerhard Fettweis |
| author_facet | Mohammad Parvini Philipp Schulz Gerhard Fettweis |
| author_sort | Mohammad Parvini |
| collection | DOAJ |
| description | As one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification of the signal of interest along the platoon chain. String stability is dependent on reliable inter-vehicle communications and proper controller design. Therefore, in this paper, we formulate radio resource management (RRM) problem with the purpose of satisfying the reliability of the vehicle-to-vehicle (V2V) links and string stability of the platoon. We tackle the optimization problem from different angles. First, we devise centralized classical approaches based on difference of two convex functions (d.c.) programming, in which we assume the base station (BS) has full knowledge over the V2V channel gains. In the second strategy, we develop decentralized resource allocation approaches based on multi-agent reinforcement learning (MARL). In essence, we model each transmitter vehicle in the platoon as an autonomous agent that tries to find an optimal policy according to its local estimated information to maximize the total expected reward. We also investigate whether the integration of federated learning (FL) with decentralized MARL algorithms can bring any potential benefits. This comparison between classical and machine learning (ML)-based RRM strategies helps us make crucial observations in terms of robustness, sensitivity, and efficacy of the policies that are learned by reinforcement learning (RL)-based resource allocation algorithms. |
| first_indexed | 2024-04-24T12:01:01Z |
| format | Article |
| id | doaj.art-93459f728c534f32b43e7d7e6b5774ac |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-04-24T12:01:01Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-93459f728c534f32b43e7d7e6b5774ac2024-04-08T23:01:25ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0151958197410.1109/OJCOMS.2024.338050910477602Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based SolutionsMohammad Parvini0https://orcid.org/0000-0002-1315-7635Philipp Schulz1https://orcid.org/0000-0002-0738-556XGerhard Fettweis2https://orcid.org/0000-0003-4622-1311Vodafone Chair Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyVodafone Chair Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyVodafone Chair Mobile Communications Systems, Technische Universität Dresden, Dresden, GermanyAs one of the promising intelligent transportation frameworks, vehicular platooning has the potential to bring about sustainable and efficient mobility solutions. One of the challenges in the development of platooning is maintaining the string stability, which ensures that there is no amplification of the signal of interest along the platoon chain. String stability is dependent on reliable inter-vehicle communications and proper controller design. Therefore, in this paper, we formulate radio resource management (RRM) problem with the purpose of satisfying the reliability of the vehicle-to-vehicle (V2V) links and string stability of the platoon. We tackle the optimization problem from different angles. First, we devise centralized classical approaches based on difference of two convex functions (d.c.) programming, in which we assume the base station (BS) has full knowledge over the V2V channel gains. In the second strategy, we develop decentralized resource allocation approaches based on multi-agent reinforcement learning (MARL). In essence, we model each transmitter vehicle in the platoon as an autonomous agent that tries to find an optimal policy according to its local estimated information to maximize the total expected reward. We also investigate whether the integration of federated learning (FL) with decentralized MARL algorithms can bring any potential benefits. This comparison between classical and machine learning (ML)-based RRM strategies helps us make crucial observations in terms of robustness, sensitivity, and efficacy of the policies that are learned by reinforcement learning (RL)-based resource allocation algorithms.https://ieeexplore.ieee.org/document/10477602/Difference of two convex functions (d.c.) programmingoptimizationmulti-agent reinforcement learning (MARL)platooningradio resource management (RRM)federated learning (FL) |
| spellingShingle | Mohammad Parvini Philipp Schulz Gerhard Fettweis Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions IEEE Open Journal of the Communications Society Difference of two convex functions (d.c.) programming optimization multi-agent reinforcement learning (MARL) platooning radio resource management (RRM) federated learning (FL) |
| title | Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions |
| title_full | Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions |
| title_fullStr | Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions |
| title_full_unstemmed | Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions |
| title_short | Resource Allocation in V2X Networks: From Classical Optimization to Machine Learning-Based Solutions |
| title_sort | resource allocation in v2x networks from classical optimization to machine learning based solutions |
| topic | Difference of two convex functions (d.c.) programming optimization multi-agent reinforcement learning (MARL) platooning radio resource management (RRM) federated learning (FL) |
| url | https://ieeexplore.ieee.org/document/10477602/ |
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