Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks
Mobile Edge Computing (MEC) has a widely established merit of bringing powerful computing servers to geographically closer locations to computationally limited devices; hence, reducing the task offloading latency from these devices to the servers. However, the frequent communication between devices...
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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/10418471/ |
| _version_ | 1797296370765266944 |
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| author | An Huang Long Qu Maurice J. Khabbaz |
| author_facet | An Huang Long Qu Maurice J. Khabbaz |
| author_sort | An Huang |
| collection | DOAJ |
| description | Mobile Edge Computing (MEC) has a widely established merit of bringing powerful computing servers to geographically closer locations to computationally limited devices; hence, reducing the task offloading latency from these devices to the servers. However, the frequent communication between devices and edge servers increases the network-wide traffic, therefore, stands in the way of enjoying notable improvements in network-wide latency. The optimization of this problem becomes of utmost importance to boldly underline the advantages of MEC-based task offloading. Reconfigurable Intelligent Surfaces (RISs) offer the potential to improve wireless transmission environment, thereby contributing to achieving this objective. This paper addresses a network-wide latency optimization problem in the context of multi-RIS-assisted MEC offloading scenarios. This problem is, herein, subdivided into two sub-problems, namely: <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>) path selection and <inline-formula> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula>) joint optimization of RIS phases and offloading volume. The cascaded channel gain is derived for <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>). A balanced trade-off is sought between passive beamforming gain and reflection loss by reformulating <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>) as a shortest path problem using Graph Theory (GT). <inline-formula> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula>) requires a different approach than the typical Successive Convex Approximation (SCA) technique used for single RIS. Instead, a Semi-Definite Relaxation (SDR) method is adopted to transform the non-convex sub-problem into a convex one, solved efficiently using an Alternating Optimization (AO) approach. The results of extensive simulations and numerical analyses reported herein reveal the notable latency reduction potency of multi-RIS-assisted MEC systems by, respectively, 24% as compared to their RIS-agnostic counterparts, and, by 15.34% when compared to systems incorporating a single RIS. |
| first_indexed | 2024-03-07T22:03:41Z |
| format | Article |
| id | doaj.art-dead642cb7c440cb88891941c1189089 |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-03-07T22:03:41Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-dead642cb7c440cb88891941c11890892024-02-24T00:01:29ZengIEEEIEEE Open Journal of the Communications Society2644-125X2024-01-0151204122110.1109/OJCOMS.2024.336138310418471Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge NetworksAn Huang0https://orcid.org/0009-0002-6299-0778Long Qu1https://orcid.org/0000-0002-4246-7421Maurice J. Khabbaz2Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaFaculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, ChinaComputer Science Department, American University of Beirut, Beirut, LebanonMobile Edge Computing (MEC) has a widely established merit of bringing powerful computing servers to geographically closer locations to computationally limited devices; hence, reducing the task offloading latency from these devices to the servers. However, the frequent communication between devices and edge servers increases the network-wide traffic, therefore, stands in the way of enjoying notable improvements in network-wide latency. The optimization of this problem becomes of utmost importance to boldly underline the advantages of MEC-based task offloading. Reconfigurable Intelligent Surfaces (RISs) offer the potential to improve wireless transmission environment, thereby contributing to achieving this objective. This paper addresses a network-wide latency optimization problem in the context of multi-RIS-assisted MEC offloading scenarios. This problem is, herein, subdivided into two sub-problems, namely: <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>) path selection and <inline-formula> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula>) joint optimization of RIS phases and offloading volume. The cascaded channel gain is derived for <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>). A balanced trade-off is sought between passive beamforming gain and reflection loss by reformulating <inline-formula> <tex-math notation="LaTeX">${a}$ </tex-math></inline-formula>) as a shortest path problem using Graph Theory (GT). <inline-formula> <tex-math notation="LaTeX">${b}$ </tex-math></inline-formula>) requires a different approach than the typical Successive Convex Approximation (SCA) technique used for single RIS. Instead, a Semi-Definite Relaxation (SDR) method is adopted to transform the non-convex sub-problem into a convex one, solved efficiently using an Alternating Optimization (AO) approach. The results of extensive simulations and numerical analyses reported herein reveal the notable latency reduction potency of multi-RIS-assisted MEC systems by, respectively, 24% as compared to their RIS-agnostic counterparts, and, by 15.34% when compared to systems incorporating a single RIS.https://ieeexplore.ieee.org/document/10418471/Reconfigurable intelligent surface (RIS)mobile edge computinglatency minimizationalternating optimization |
| spellingShingle | An Huang Long Qu Maurice J. Khabbaz Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks IEEE Open Journal of the Communications Society Reconfigurable intelligent surface (RIS) mobile edge computing latency minimization alternating optimization |
| title | Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks |
| title_full | Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks |
| title_fullStr | Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks |
| title_full_unstemmed | Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks |
| title_short | Latency-Aware Computation Offloading in Multi-RIS-Assisted Edge Networks |
| title_sort | latency aware computation offloading in multi ris assisted edge networks |
| topic | Reconfigurable intelligent surface (RIS) mobile edge computing latency minimization alternating optimization |
| url | https://ieeexplore.ieee.org/document/10418471/ |
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