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- Publisher Website: 10.1109/JIOT.2022.3178983
- Scopus: eid_2-s2.0-85131770197
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Article: Computation Capacity Enhancement by Joint UAV and RIS Design in IoT
Title | Computation Capacity Enhancement by Joint UAV and RIS Design in IoT |
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Authors | |
Keywords | Mobile-edge computing (MEC) reconfigurable intelligence surface (RIS) trajectory optimization unmanned aerial vehicle (UAV) |
Issue Date | 2022 |
Citation | IEEE Internet of Things Journal, 2022, v. 9, n. 20, p. 20590-20603 How to Cite? |
Abstract | Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max-min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV's trajectory. We develop a concave-convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max-min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement. |
Persistent Identifier | http://hdl.handle.net/10722/349731 |
DC Field | Value | Language |
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dc.contributor.author | Xu, Yu | - |
dc.contributor.author | Zhang, Tiankui | - |
dc.contributor.author | Liu, Yuanwei | - |
dc.contributor.author | Yang, Dingcheng | - |
dc.contributor.author | Xiao, Lin | - |
dc.contributor.author | Tao, Meixia | - |
dc.date.accessioned | 2024-10-17T07:00:26Z | - |
dc.date.available | 2024-10-17T07:00:26Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | IEEE Internet of Things Journal, 2022, v. 9, n. 20, p. 20590-20603 | - |
dc.identifier.uri | http://hdl.handle.net/10722/349731 | - |
dc.description.abstract | Mobile-edge computing (MEC) networks are facing limited coverage and harsh wireless transmission environments that severely hinder the computation capacity of the Internet-of-Things (IoT) devices. To overcome these issues, this article proposes a novel MEC framework empowered by an unmanned aerial vehicle (UAV) relay and a reconfigurable intelligence surface (RIS). To fully exploit the potentials in terms of computation enhancement brought by the joint UAV and RIS design, we formulate a max-min computation capacity problem via determining the uplink signal detection, active beamforming of UAV, passive beamforming of RIS, time slot partition, computation bits of UAV, and UAV's trajectory. We develop a concave-convex procedure (CCCP)-based algorithm in an alternating optimization manner over three subproblems to solve the formulated problem. It finds that the CCCP-based algorithm is conducive to decouple the intractable expressions by converting them into new but tractable second-order cone (SOC) constrains. To evaluate the performance of the proposed CCCP-based algorithm, we later design a direct algorithm by exploiting the implicit convexity of the problem. Simulation results demonstrate that the proposed CCCP-based algorithm derives a comparable performance as the direct algorithm, and achieves about 2.57-Mb max-min computation capacity higher compared with the straight flight case, and 8.08-Mb max-min computation capacity higher compared with the case without RIS, which validate the superiority of the joint UAV and RIS design for computation enhancement. | - |
dc.language | eng | - |
dc.relation.ispartof | IEEE Internet of Things Journal | - |
dc.subject | Mobile-edge computing (MEC) | - |
dc.subject | reconfigurable intelligence surface (RIS) | - |
dc.subject | trajectory optimization | - |
dc.subject | unmanned aerial vehicle (UAV) | - |
dc.title | Computation Capacity Enhancement by Joint UAV and RIS Design in IoT | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/JIOT.2022.3178983 | - |
dc.identifier.scopus | eid_2-s2.0-85131770197 | - |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 20 | - |
dc.identifier.spage | 20590 | - |
dc.identifier.epage | 20603 | - |
dc.identifier.eissn | 2327-4662 | - |