QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning Approach Article Swipe
Ali Parsa
,
Neda Moghim
,
Sachin Shetty
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3593836
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/ojcoms.2025.3593836
Modern wireless communication systems face increasingly complex challenges due to rapidly changing channel conditions and the growing diversity of application-specific Quality of Service (QoS) requirements. Traditional link adaptation mechanisms primarily aim to maximize throughput and often lack the flexibility to support emerging applications, such as Extended Reality (XR) and Virtual Reality (VR), which demand simultaneous guarantees for high data rates, ultra low latency, and high reliability. These stringent and multidimensional QoS needs call for more intelligent and adaptive solutions. In this paper, we propose QDRLLA (QoS-aware Deep Reinforcement Learning-based Link Adaptation), a novel framework that employs deep reinforcement learning to dynamically adjust key link parameters, including modulation and coding schemes, transmission power, and subcarrier spacing, based on the QoS requirements of each application. QDRLLA learns from the environment and past observations to make informed decisions that go beyond conventional heuristic-based methods. Through extensive simulations, we demonstrate that QDRLLA significantly improves compliance with QoS targets across a range of network conditions and application types. It also improves energy efficiency by avoiding unnecessary retransmissions and optimizing resource usage. These results underscore the effectiveness of QDRLLA in supporting the complex service requirements of next-generation wireless networks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/ojcoms.2025.3593836
- OA Status
- gold
- References
- 22
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4412747727Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/ojcoms.2025.3593836Digital Object Identifier
- Title
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QoS-Aware Link Adaptation for Beyond 5G Networks: A Deep Reinforcement Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Ali Parsa, Neda Moghim, Sachin ShettyList of authors in order
- Landing page
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https://doi.org/10.1109/ojcoms.2025.3593836Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/ojcoms.2025.3593836Direct OA link when available
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Reinforcement learning, Adaptation (eye), Computer science, Link (geometry), Link adaptation, Computer network, Quality of service, Distributed computing, Artificial intelligence, Psychology, Neuroscience, Fading, Channel (broadcasting)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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22Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.range | 156 |
| abstract_inverted_index.ultra | 60 |
| abstract_inverted_index.which | 52 |
| abstract_inverted_index.Modern | 0 |
| abstract_inverted_index.QDRLLA | 84, 123, 147, 182 |
| abstract_inverted_index.across | 154 |
| abstract_inverted_index.adjust | 101 |
| abstract_inverted_index.beyond | 137 |
| abstract_inverted_index.coding | 108 |
| abstract_inverted_index.demand | 53 |
| abstract_inverted_index.energy | 166 |
| abstract_inverted_index.learns | 124 |
| abstract_inverted_index.paper, | 81 |
| abstract_inverted_index.power, | 111 |
| abstract_inverted_index.rates, | 59 |
| abstract_inverted_index.types. | 162 |
| abstract_inverted_index.usage. | 175 |
| abstract_inverted_index.Quality | 20 |
| abstract_inverted_index.Reality | 46, 50 |
| abstract_inverted_index.Service | 22 |
| abstract_inverted_index.Through | 141 |
| abstract_inverted_index.Virtual | 49 |
| abstract_inverted_index.channel | 12 |
| abstract_inverted_index.complex | 6, 186 |
| abstract_inverted_index.employs | 95 |
| abstract_inverted_index.growing | 16 |
| abstract_inverted_index.network | 158 |
| abstract_inverted_index.propose | 83 |
| abstract_inverted_index.rapidly | 10 |
| abstract_inverted_index.results | 177 |
| abstract_inverted_index.service | 187 |
| abstract_inverted_index.support | 40 |
| abstract_inverted_index.systems | 3 |
| abstract_inverted_index.targets | 153 |
| abstract_inverted_index.Extended | 45 |
| abstract_inverted_index.adaptive | 77 |
| abstract_inverted_index.avoiding | 169 |
| abstract_inverted_index.changing | 11 |
| abstract_inverted_index.emerging | 41 |
| abstract_inverted_index.improves | 149, 165 |
| abstract_inverted_index.informed | 133 |
| abstract_inverted_index.latency, | 62 |
| abstract_inverted_index.learning | 98 |
| abstract_inverted_index.maximize | 32 |
| abstract_inverted_index.methods. | 140 |
| abstract_inverted_index.resource | 174 |
| abstract_inverted_index.schemes, | 109 |
| abstract_inverted_index.spacing, | 114 |
| abstract_inverted_index.wireless | 1, 191 |
| abstract_inverted_index.decisions | 134 |
| abstract_inverted_index.diversity | 17 |
| abstract_inverted_index.extensive | 142 |
| abstract_inverted_index.framework | 93 |
| abstract_inverted_index.including | 105 |
| abstract_inverted_index.networks. | 192 |
| abstract_inverted_index.primarily | 29 |
| abstract_inverted_index.stringent | 67 |
| abstract_inverted_index.(QoS-aware | 85 |
| abstract_inverted_index.adaptation | 27 |
| abstract_inverted_index.challenges | 7 |
| abstract_inverted_index.compliance | 150 |
| abstract_inverted_index.conditions | 13, 159 |
| abstract_inverted_index.efficiency | 167 |
| abstract_inverted_index.guarantees | 55 |
| abstract_inverted_index.mechanisms | 28 |
| abstract_inverted_index.modulation | 106 |
| abstract_inverted_index.optimizing | 173 |
| abstract_inverted_index.solutions. | 78 |
| abstract_inverted_index.subcarrier | 113 |
| abstract_inverted_index.supporting | 184 |
| abstract_inverted_index.throughput | 33 |
| abstract_inverted_index.underscore | 178 |
| abstract_inverted_index.Traditional | 25 |
| abstract_inverted_index.application | 161 |
| abstract_inverted_index.demonstrate | 145 |
| abstract_inverted_index.dynamically | 100 |
| abstract_inverted_index.environment | 127 |
| abstract_inverted_index.flexibility | 38 |
| abstract_inverted_index.intelligent | 75 |
| abstract_inverted_index.parameters, | 104 |
| abstract_inverted_index.unnecessary | 170 |
| abstract_inverted_index.Adaptation), | 90 |
| abstract_inverted_index.application. | 122 |
| abstract_inverted_index.conventional | 138 |
| abstract_inverted_index.increasingly | 5 |
| abstract_inverted_index.observations | 130 |
| abstract_inverted_index.reliability. | 65 |
| abstract_inverted_index.requirements | 119, 188 |
| abstract_inverted_index.simulations, | 143 |
| abstract_inverted_index.simultaneous | 54 |
| abstract_inverted_index.transmission | 110 |
| abstract_inverted_index.Reinforcement | 87 |
| abstract_inverted_index.applications, | 42 |
| abstract_inverted_index.communication | 2 |
| abstract_inverted_index.effectiveness | 180 |
| abstract_inverted_index.reinforcement | 97 |
| abstract_inverted_index.requirements. | 24 |
| abstract_inverted_index.significantly | 148 |
| abstract_inverted_index.Learning-based | 88 |
| abstract_inverted_index.heuristic-based | 139 |
| abstract_inverted_index.next-generation | 190 |
| abstract_inverted_index.retransmissions | 171 |
| abstract_inverted_index.multidimensional | 69 |
| abstract_inverted_index.application-specific | 19 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.3320784 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |