Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X Communications Article Swipe
Pyae Sone Aung
,
Loc X. Nguyen
,
Yan Kyaw Tun
,
Zhu Han
,
Choong Seon Hong
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.08279
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2308.08279
Vehicle-to-Everything (V2X) communications play a crucial role in ensuring safe and efficient modern transportation systems. However, challenges arise in scenarios with buildings, leading to signal obstruction and coverage limitations. To alleviate these challenges, reconfigurable intelligent surface (RIS) is regarded as an effective solution for communication performance by tuning passive signal reflection. RIS has acquired prominence in 6G networks due to its improved spectral efficiency, simple deployment, and cost-effectiveness. Nevertheless, conventional RIS solutions have coverage limitations. Therefore, researchers have started focusing on the promising concept of simultaneously transmitting and reflecting RIS (STAR-RIS), which provides 360\degree coverage while utilizing the advantages of RIS technology. In this paper, a STAR-RIS-assisted V2X communication system is investigated. An optimization problem is formulated to maximize the achievable data rate for vehicle-to-infrastructure (V2I) users while satisfying the latency and reliability requirements of vehicle-to-vehicle (V2V) pairs by jointly optimizing the spectrum allocation, amplitudes, and phase shifts of STAR-RIS elements, digital beamforming vectors for V2I links, and transmit power for V2V pairs. Since it is challenging to solve in polynomial time, we decompose our problem into two sub-problems. For the first sub-problem, we model the control variables as a Markov Decision Process (MDP) and propose a combined double deep Q-network (DDQN) with an attention mechanism so that the model can potentially focus on relevant inputs. For the latter, a standard optimization-based approach is implemented to provide a real-time solution, reducing computational costs. Extensive numerical analysis is developed to demonstrate the superiority of our proposed algorithm compared to benchmark schemes.
Related Topics
Concepts
Computer science
Markov decision process
Beamforming
Reinforcement learning
Spectral efficiency
Wireless
Software deployment
Mathematical optimization
Optimization problem
Distributed computing
Real-time computing
Markov process
Telecommunications
Algorithm
Artificial intelligence
Mathematics
Operating system
Statistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2308.08279
- https://arxiv.org/pdf/2308.08279
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385965831
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385965831Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2308.08279Digital Object Identifier
- Title
-
Deep Reinforcement Learning based Joint Spectrum Allocation and Configuration Design for STAR-RIS-Assisted V2X CommunicationsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-08-16Full publication date if available
- Authors
-
Pyae Sone Aung, Loc X. Nguyen, Yan Kyaw Tun, Zhu Han, Choong Seon HongList of authors in order
- Landing page
-
https://arxiv.org/abs/2308.08279Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2308.08279Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2308.08279Direct OA link when available
- Concepts
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Computer science, Markov decision process, Beamforming, Reinforcement learning, Spectral efficiency, Wireless, Software deployment, Mathematical optimization, Optimization problem, Distributed computing, Real-time computing, Markov process, Telecommunications, Algorithm, Artificial intelligence, Mathematics, Operating system, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.double | 198 |
| abstract_inverted_index.links, | 156 |
| abstract_inverted_index.modern | 12 |
| abstract_inverted_index.pairs. | 162 |
| abstract_inverted_index.paper, | 104 |
| abstract_inverted_index.shifts | 147 |
| abstract_inverted_index.signal | 24, 49 |
| abstract_inverted_index.simple | 64 |
| abstract_inverted_index.system | 109 |
| abstract_inverted_index.tuning | 47 |
| abstract_inverted_index.Process | 192 |
| abstract_inverted_index.concept | 83 |
| abstract_inverted_index.control | 186 |
| abstract_inverted_index.crucial | 5 |
| abstract_inverted_index.digital | 151 |
| abstract_inverted_index.inputs. | 215 |
| abstract_inverted_index.jointly | 139 |
| abstract_inverted_index.latency | 130 |
| abstract_inverted_index.latter, | 218 |
| abstract_inverted_index.leading | 22 |
| abstract_inverted_index.passive | 48 |
| abstract_inverted_index.problem | 114, 175 |
| abstract_inverted_index.propose | 195 |
| abstract_inverted_index.provide | 226 |
| abstract_inverted_index.started | 78 |
| abstract_inverted_index.surface | 35 |
| abstract_inverted_index.vectors | 153 |
| abstract_inverted_index.Decision | 191 |
| abstract_inverted_index.However, | 15 |
| abstract_inverted_index.STAR-RIS | 149 |
| abstract_inverted_index.acquired | 53 |
| abstract_inverted_index.analysis | 235 |
| abstract_inverted_index.approach | 222 |
| abstract_inverted_index.combined | 197 |
| abstract_inverted_index.compared | 246 |
| abstract_inverted_index.coverage | 27, 73, 94 |
| abstract_inverted_index.ensuring | 8 |
| abstract_inverted_index.focusing | 79 |
| abstract_inverted_index.improved | 61 |
| abstract_inverted_index.maximize | 118 |
| abstract_inverted_index.networks | 57 |
| abstract_inverted_index.proposed | 244 |
| abstract_inverted_index.provides | 92 |
| abstract_inverted_index.reducing | 230 |
| abstract_inverted_index.regarded | 38 |
| abstract_inverted_index.relevant | 214 |
| abstract_inverted_index.schemes. | 249 |
| abstract_inverted_index.solution | 42 |
| abstract_inverted_index.spectral | 62 |
| abstract_inverted_index.spectrum | 142 |
| abstract_inverted_index.standard | 220 |
| abstract_inverted_index.systems. | 14 |
| abstract_inverted_index.transmit | 158 |
| abstract_inverted_index.Extensive | 233 |
| abstract_inverted_index.Q-network | 200 |
| abstract_inverted_index.algorithm | 245 |
| abstract_inverted_index.alleviate | 30 |
| abstract_inverted_index.attention | 204 |
| abstract_inverted_index.benchmark | 248 |
| abstract_inverted_index.decompose | 173 |
| abstract_inverted_index.developed | 237 |
| abstract_inverted_index.effective | 41 |
| abstract_inverted_index.efficient | 11 |
| abstract_inverted_index.elements, | 150 |
| abstract_inverted_index.mechanism | 205 |
| abstract_inverted_index.numerical | 234 |
| abstract_inverted_index.promising | 82 |
| abstract_inverted_index.real-time | 228 |
| abstract_inverted_index.scenarios | 19 |
| abstract_inverted_index.solution, | 229 |
| abstract_inverted_index.solutions | 71 |
| abstract_inverted_index.utilizing | 96 |
| abstract_inverted_index.variables | 187 |
| abstract_inverted_index.360\degree | 93 |
| abstract_inverted_index.Therefore, | 75 |
| abstract_inverted_index.achievable | 120 |
| abstract_inverted_index.advantages | 98 |
| abstract_inverted_index.buildings, | 21 |
| abstract_inverted_index.challenges | 16 |
| abstract_inverted_index.formulated | 116 |
| abstract_inverted_index.optimizing | 140 |
| abstract_inverted_index.polynomial | 170 |
| abstract_inverted_index.prominence | 54 |
| abstract_inverted_index.reflecting | 88 |
| abstract_inverted_index.satisfying | 128 |
| abstract_inverted_index.(STAR-RIS), | 90 |
| abstract_inverted_index.allocation, | 143 |
| abstract_inverted_index.amplitudes, | 144 |
| abstract_inverted_index.beamforming | 152 |
| abstract_inverted_index.challenges, | 32 |
| abstract_inverted_index.challenging | 166 |
| abstract_inverted_index.demonstrate | 239 |
| abstract_inverted_index.deployment, | 65 |
| abstract_inverted_index.efficiency, | 63 |
| abstract_inverted_index.implemented | 224 |
| abstract_inverted_index.intelligent | 34 |
| abstract_inverted_index.obstruction | 25 |
| abstract_inverted_index.performance | 45 |
| abstract_inverted_index.potentially | 211 |
| abstract_inverted_index.reflection. | 50 |
| abstract_inverted_index.reliability | 132 |
| abstract_inverted_index.researchers | 76 |
| abstract_inverted_index.superiority | 241 |
| abstract_inverted_index.technology. | 101 |
| abstract_inverted_index.conventional | 69 |
| abstract_inverted_index.limitations. | 28, 74 |
| abstract_inverted_index.optimization | 113 |
| abstract_inverted_index.requirements | 133 |
| abstract_inverted_index.sub-problem, | 182 |
| abstract_inverted_index.transmitting | 86 |
| abstract_inverted_index.Nevertheless, | 68 |
| abstract_inverted_index.communication | 44, 108 |
| abstract_inverted_index.computational | 231 |
| abstract_inverted_index.investigated. | 111 |
| abstract_inverted_index.sub-problems. | 178 |
| abstract_inverted_index.communications | 2 |
| abstract_inverted_index.reconfigurable | 33 |
| abstract_inverted_index.simultaneously | 85 |
| abstract_inverted_index.transportation | 13 |
| abstract_inverted_index.STAR-RIS-assisted | 106 |
| abstract_inverted_index.optimization-based | 221 |
| abstract_inverted_index.vehicle-to-vehicle | 135 |
| abstract_inverted_index.cost-effectiveness. | 67 |
| abstract_inverted_index.Vehicle-to-Everything | 0 |
| abstract_inverted_index.vehicle-to-infrastructure | 124 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6399999856948853 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile |