Resource Allocation for Full-Duplex-Enabled Cognitive Backscatter Networks

doi:10.1109/TWC.2019.2912203

2019-06-01

PRE

Sa Xiao

Huayan Guo

Ying‐Chang Liang *

DOI：10.1109/TWC.2019.2912203

原文链接

原文求助

摘要

En 中文

Ambient backscatter communications (AmBC) enable wireless communications riding on ambient radio frequency (RF) signals instead of self-generated RF signals. Therefore, it has been considered as a promising candidate for the future Internet-of-Things with stringent energy and spectrum constraints. In this paper, we investigate a full-duplex-enabled cognitive backscatter network, in which an AmBC system underlays a primary cellular system, and the primary access point can transmit primary signals and receive backscatter signals simultaneously via full-duplex communications. We aim to maximize the throughput of the AmBC system while guaranteeing the minimum rate requirements of the primary system via joint time scheduling, transmit power allocation, and reflection coefficient (RC) adjustment. To solve the problem, we propose an iterative method utilizing block coordinated decent to partition the variables into the time scheduling variable and the joint transmit power allocation and RC adjustment variable. For the time scheduling problem, we first prove its convexity and then utilize the interior-point method to solve it. For the joint power allocation and RC adjustment problem, we resort to the concave-convex procedure to transform it into a sequence of convex optimization problems, and then adopt Lagrange dual decomposition to tackle these convex optimization problems. The simulation results demonstrate that the proposed method can significantly increase the throughput of the AmBC system with a fast convergence speed.

Keyword:

Ambient backscatter communications

spectrum sharing

resource allocation