Distributed Power Control for Carrier Aggregation in Cognitive Heterogeneous 5G Cellular Networks

In this paper, we study the distributed optimal power allocation for the carrier aggregation in next generation (5G) cognitive radio networks. The presented study relies on the power control and carrier aggregation principles of wireless communication systems. Our approach differs from the conventional well-known water filling (WF) algorithm in the sense that we provide decentralized solution, wherein all of the Lagrange multipliers are not handled equally over the heterogeneous fading channels. This is accomplished in order to provide distributed power control over the heterogeneous fading channels that are considered non-identically distributed and non-identical Nakagami-m channels. To this end, we first formulate the optimization problem and in the sequel, we solve it using the alternating direction method of multipliers (ADMM), which provides to our solution the required decomposition for each channel and the robustness through the augmented Lagrangian. For benchmarking, we provide comparison to other prominent decomposition methods like dual decomposition method (DDM). Simulation results highlight the performance gain of ADMM in terms of number of iterations. The achievable sum rates are also depicted for different network setups. Comparison to the WF is also provided that reveals the gain of the applied decomposition methods (i.e. ADMM and DDM) to the cognitive heterogeneous 5G cellular networks.