Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866916159971917824 |
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| author | Mahmood, Asad Vu, Thang X. Khan, Wali Ullah Chatzinotas, Symeon Ottersten, Björn |
| author_facet | Mahmood, Asad Vu, Thang X. Khan, Wali Ullah Chatzinotas, Symeon Ottersten, Björn |
| contents | Recent advancements in 6G systems signal a leap towards universal connectivity and ultra-reliable, low-latency communications for real-time data devices. Yet, these advancements encounter obstacles such as limited device battery life and computational power, along with urban signal blockages. To counter these, Intelligent Reconfigurable Surfaces (IRS) within Mobile Edge Cloud (MEC) infrastructures offer enhanced computing to overcome device limitations and create alternative communication paths. Despite these improvements, connectivity issues remain for remote areas. Our paper presents the Beyond Diagonal IRS (BD-IRS or IRS 2.0), integrated with UAVs in MEC networks (BD-IRS-UAV), providing on-demand links for remote users to offload tasks, tackling resource and battery limitations. We propose a joint optimization strategy to reduce system's worst-case latency and UAV hovering time by optimizing BD-IRS-UAV deployment and resource allocation. This challenge is approached by dividing it into two sub-problems: BD-IRS-UAV Placement and Computational Resource Optimization, and Communication Resource Optimization, each solved iteratively. This design significantly enhances system performance, showing a $17.75\%$ increase over traditional diagonal IRS and a $25.43\%$ improvement over IRS on buildings, with a $13.44\%$ enhancement in worst-case latency compared to binary offloading schemes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_07199 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks Mahmood, Asad Vu, Thang X. Khan, Wali Ullah Chatzinotas, Symeon Ottersten, Björn Signal Processing Recent advancements in 6G systems signal a leap towards universal connectivity and ultra-reliable, low-latency communications for real-time data devices. Yet, these advancements encounter obstacles such as limited device battery life and computational power, along with urban signal blockages. To counter these, Intelligent Reconfigurable Surfaces (IRS) within Mobile Edge Cloud (MEC) infrastructures offer enhanced computing to overcome device limitations and create alternative communication paths. Despite these improvements, connectivity issues remain for remote areas. Our paper presents the Beyond Diagonal IRS (BD-IRS or IRS 2.0), integrated with UAVs in MEC networks (BD-IRS-UAV), providing on-demand links for remote users to offload tasks, tackling resource and battery limitations. We propose a joint optimization strategy to reduce system's worst-case latency and UAV hovering time by optimizing BD-IRS-UAV deployment and resource allocation. This challenge is approached by dividing it into two sub-problems: BD-IRS-UAV Placement and Computational Resource Optimization, and Communication Resource Optimization, each solved iteratively. This design significantly enhances system performance, showing a $17.75\%$ increase over traditional diagonal IRS and a $25.43\%$ improvement over IRS on buildings, with a $13.44\%$ enhancement in worst-case latency compared to binary offloading schemes. |
| title | Joint Computation and Communication Resource Optimization for Beyond Diagonal UAV-IRS Empowered MEC Networks |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2311.07199 |