Distributed Power Allocation for Multi-Flow Carrier Aggregation in Heterogeneous Cognitive Cellular Networks

Abstract

In this paper, we study distributed power allocation for multiflow carrier aggregation in cognitive cellular networks. Our approach differs from the conventional water filling (WF) algorithm since we deal with heterogeneous fading channels, wherein all of the Lagrange multipliers are not handled equally over the heterogeneous cells. The distributed power control solution is carried out over heterogeneous fading channels that are considered nonidentically distributed Nakagami- $m$ fading channels. We first formulate the optimization problem, and we then solve it using the alternating direction method of multipliers (ADMM), which allows the required decomposition for each channel and the required statistical learning among the different subproblem solutions. We also provide comparison with the dual decomposition method and WF solutions considered as solutions without cognition. Simulation results highlight the performance gain of ADMM in terms of the number of iterations. Furthermore, we provide a multiuser application scenario, where the analysis on the fading channel model with Nakagami- $m$ distribution is carried out. We distinguish into the synchronous and the asynchronous ADMM implementations tackling the asynchronous user updates that can be found in a heterogeneous network deployment. The simulation results are obtained to highlight the impact of the partial barrier and the bounded delay in the asynchronous use case.