Toward 6G: Latency-Optimized MEC Systems with UAV and RIS Integration
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| Publicado no: | Mathematics vol. 13, no. 5 (2025), p. 871 |
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| Autor principal: | |
| Publicado em: |
MDPI AG
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| Assuntos: | |
| Acesso em linha: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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MARC
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| 022 | |a 2227-7390 | ||
| 024 | 7 | |a 10.3390/math13050871 |2 doi | |
| 035 | |a 3176344728 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231533 |2 nlm | ||
| 100 | 1 | |a Alshahrani, Abdullah | |
| 245 | 1 | |a Toward 6G: Latency-Optimized MEC Systems with UAV and RIS Integration | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Multi-access edge computing (MEC) has emerged as a cornerstone technology for deploying 6G network services, offering efficient computation and ultra-low-latency communication. The integration of unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) further enhances wireless propagation, capacity, and coverage, presenting a transformative paradigm for next-generation networks. This paper addresses the critical challenge of task offloading and resource allocation in an MEC-based system, where a massive MIMO base station, serving multiple macro-cells, hosts the MEC server with support from a UAV-equipped RIS. We propose an optimization framework to minimize task execution latency for user equipment (UE) by jointly optimizing task offloading and communication resource allocation within this UAV-assisted, RIS-aided network. By modeling this problem as a Markov decision process (MDP) with a discrete-continuous hybrid action space, we develop a deep reinforcement learning (DRL) algorithm leveraging a hybrid space representation to solve it effectively. Extensive simulations validate the superiority of the proposed method, demonstrating significant latency reductions compared to state-of-the-art approaches, thereby advancing the feasibility of MEC in 6G networks. | |
| 653 | |a Innovations | ||
| 653 | |a Propagation | ||
| 653 | |a Wireless networks | ||
| 653 | |a Technological change | ||
| 653 | |a Control algorithms | ||
| 653 | |a 6G mobile communication | ||
| 653 | |a Communication | ||
| 653 | |a Unmanned aerial vehicles | ||
| 653 | |a Markov processes | ||
| 653 | |a Optimization | ||
| 653 | |a Edge computing | ||
| 653 | |a Resource allocation | ||
| 653 | |a Network latency | ||
| 653 | |a Mobile computing | ||
| 653 | |a Computation offloading | ||
| 653 | |a Algorithms | ||
| 653 | |a Linear programming | ||
| 653 | |a Machine learning | ||
| 653 | |a Energy consumption | ||
| 653 | |a Internet of Things | ||
| 653 | |a Efficiency | ||
| 653 | |a Reconfigurable intelligent surfaces | ||
| 773 | 0 | |t Mathematics |g vol. 13, no. 5 (2025), p. 871 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176344728/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176344728/fulltextwithgraphics/embedded/6A8EOT78XXH2IG52?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176344728/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch |