Journal
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
Volume 70, Issue 6, Pages 6200-6205Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TVT.2021.3077094
Keywords
Task analysis; Resource management; Computational modeling; Backscatter; Servers; Energy consumption; Edge computing; Backscatter communications; wireless-powered technology; multi-access edge computing; resource allocation
Categories
Funding
- National Natural Science Foundation of China [61601071, 62071078]
- Natural Science Foundation of Chongqing [cstc2019jcyjxfkxX0002]
- Chongqing Entrepreneurship and Innovation Program for the Returned Overseas Chinese Scholars [cx2020095, cx2020059]
- Graduate Scientific Research Innovation Project of Chongqing [CYS20251, CYS20253]
- Open Funding of Shaanxi Key Laboratory of Information Communication Network [ICNS201904]
- Science and Technology Development Fund, Macau SAR [0003/2019/A1, 0110/2020/A3, 0018/2019/AMJ]
- Ministry of Science and Technology of the People's Republic of China [0018/2019/AMJ]
- NSF [CNS-2007995]
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The MEC-based WP-BackComNets enable WDs to offload computation resources to MEC servers, but the limited battery capacity of WDs is a bottleneck for its development. To address this issue, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs by optimizing various parameters.
The multi-access edge computing (MEC)-based wireless-powered backscatter communication networks (WP-BackComNets) allow wireless devices (WDs) to offload computation resources to lightweight and widely deployed MEC servers with the assistance of backscatter devices (BDs), which have substantial application prospects for the emerging Internet-of-Things applications. However, the limited battery capacity of WDs is one of the bottlenecks restricting its further development. Reducing the energy consumption and the computation burden of WDs while ensuring the quality-of-service requirements is an urgent issue. To this end, a joint computation offloading and radio resource allocation problem is formulated to minimize the total energy consumption of WDs for an MEC-based WP-BackComNet by jointly optimizing user association, the transmit power and transmission time of WDs, the computational offloading coefficient of each task, and the reflection coefficients of BDs, where the circuit power consumption of BDs, the computational capabilities of WDs, and the task execution delay budgets are considered. To handle this non-convex problem, we propose an efficient algorithm to obtain a suboptimal solution. Simulation results demonstrate that the proposed scheme can effectively decrease the energy consumption compared with the benchmarks.
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