Distributed Resource Allocation for SWIPT-Enabled Cognitive Networks
With and Without Perfect CSI
Abstract
Simultaneous wireless information and power transfer (SWIPT)-enabled
cognitive networks (CRNs) is recognized as one of most promising
techniques to improve spectrum efficiency and prolong operation lifetime
in 5G and beyond. However, existing methods focus on the centralized
algorithm and the power allocation under perfect channel state
information (CSI). The analytical solution and the impact of the power
splitting (PS) on the optimal power allocation strategy are not
addressed. In addition, the influence of the PS factor on the feasible
region of transit power is rarely analyzed. In this paper, we propose a
joint power allocation and PS algorithm under perfect CSI and imperfect
CSI, respectively, for multiuser SWIPT-enabled CRNs scenarios. The power
minimization of resource allocation problem is formulated as a
multivariate nonconvex optimization which is hard to obtain the
closed-form solution. Hence, we propose a suboptimal algorithm to
alternatively optimize the power allocation and PS coefficient under the
cases of the low-harvested energy region and the high-harvested energy
region, respectively. Moreover, a closed-form distributed power
allocation and PS expressions are derived by the Lagrangian approach.
Simulation results confirm the proposed method with good robustness and
high energy efficiency.