Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks

原創

2020-07-02 20:34

文章目录

sense-and-send protocol analysis

A. UAV Trajectory Design Problem

题目:
Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks With Sense-and-Send Protocol

Citation:
J. Hu, H. Zhang and L. Song, “Reinforcement Learning for Decentralized Trajectory Design in Cellular UAV Networks With Sense-and-Send Protocol,” in IEEE Internet of Things Journal, vol. 6, no. 4, pp. 6177-6189, Aug. 2019, doi: 10.1109/JIOT.2018.2876513.

文章的 ieee 链接：
https://ieeexplore.ieee.org/document/8494742

创新点：
In literature, most works focused on either the sensing or the transmission in the UAV networks, instead of considering UAV sensing and transmission jointly.

系统模型

其中每个 UAV 对应一个事先已知的 task , 不存在 UAV-user association 的问题

回传的过程要求达到一个要求的信噪比，如果达到了，可以认为再 1 个 time slot 就可以完成传输

sense and send cycle

the process is divided into cycles, which are indexed by k. In each cycle, each UAV senses its task and then sends the collected.

每个 cycle 的结构可直观地反映在图里

beaconing :

sensory data to the BS In the beaconing phase, each UAV sends its location to the BS in its beacon through the control channel.
the BS then broadcasts to inform the UAVs of the general network settings as well as the locations of all the UAVs. : UAVs can obtain the locations of other UAVs in the beginning of each cycle.
Based on the acquired information, each UAV then decides its trajectory in the cycle and informs the BS by another beacon.

A. Sense-and-Send Cycle

beaconing结束后UAV匀速直线运动直至下一个cycle开始

transmission state :

B. Uplink Subchannel Allocation Mechanism

channel allocation state :
the BS allocates the C available uplink SCs to the UAVs with uplink requirements , in order to maximize the sum of successful transmission probabilities of the UAVs.