文章
Resource Allocation for Optimizing Energy Efficiency in NOMA-based Fog UAV Wireless Networks
链接
缩写速查
cloud radio access networks (CRANs)
fog radio access networks (FRANs)
collaboration radio signal processing (CRSP)
cooperative radio resource management (CRRM)
文章贡献
- In this article, we study resource allocation in a fog UAV wireless network, where the UAV serves as a temporary mobile base station(BS) to achieve movement of the wireless network coverage area and the fog can bring gain in data rate or network traffic.
- Fog computing aims to reduce response time through processing units located in the edge of the network
- We propose a novel UAV system architecture where FRAN and NOMA coexist. In the presented UAV wireless networks, this article considers a NOMA-based UAV to relieve limited spectrum resources, and introduces fog-based caching technology to release link and traffic burden. Accordingly, these together form a network with high energy efficiency.*
架构
- All signal processing units work centrally in the baseband unit (BBU) pool
where they can share the signaling, data, and channel status information (CSI) of the entire network. - Multiple adjacent UAVs can interact to form various kinds of topologies
- UAVs are connected to the BBU pool which processes the signal transmitted by fronthaul links which can use millimeter wave technology, while backhaul links are used to connect the macro BS and BBU pool.
- content with high popularity is stored at the edge of the network preferentially and users do not need to obtain it from traditional remote data centers.a caching strategy such as first-in and first-out can be very beneficial in improving overall network capacity
算法
-
NOMA technology based UAV Wireless Networks
考虑4个用户uplink的情况，信道质量好的可以消除信道质量不好的用户对自己的影响。(其实就是SIC解调，不明白可以查阅NOMA) -
subchannel Assignment in UAV Wireless networks
The assignment of subchannel can be considered a two-sided matching procedure according to the preference lists of both subchannels and users
swapping algorithm交换算法
其他因素
-
UAV placement
-
Caching Model
- Energy Efficiency