.png){kind=icon}
返回
Computation Bits Maximization in a Backscatter Assisted Wirelessly Powered MEC Network
.png){kind=icon}
OA
AI
DOI：10.1109/LCOMM.2020.3027294.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This letter introduces a backscatter assisted wirelessly powered mobile edge computing (MEC) network, where each edge user (EU) can offload task bits to the MEC server via hybrid harvest-then-transmit (HTT) and backscatter communications. In particular, considering a practical non-linear energy harvesting (EH) model and a partial offloading scheme at each EU, we propose a scheme to maximize the weighted sum computation bits of all the EUs by jointly optimizing the backscatter reflection coefficient and time, active transmission power and time, local computing frequency and execution time of each EU. By introducing a series of auxiliary variables and using the properties of the non-linear EH model, we transform the original non-convex problem into a convex one and derive closed-form expressions for parts of the optimal solutions. Simulation results demonstrate the advantage of the proposed scheme over benchmark schemes in terms of weighted sum computation bits.
Keyword:
Task analysis
Servers
Computational modeling
Backscatter
Resource management
Integrated circuit modeling
Time-frequency analysis
Mobile edge computing
backscatter communications
partial offloading
sum computation bits
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.4
论文数:
1.3W
被引数:
2.2W
机构
引用论文
.png){kind=icon}
Energy-Efficient Resource Allocation for Wirelessly Powered Backscatter Communications无线供电反向散射通信的高能效资源分配OAAI
OAAI分享收藏Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks用于无线供电的移动边缘计算网络中在线计算卸载的深度强化学习OAAI
OAAI分享收藏
查看更多内容