User-Centric Online Learning of Power Allocation in H-CRAN

In this paper, we investigate a power control of uplink connection in Heterogeneous Cloud-Radio Access Network (H-CRAN). Our main objective is to optimize Online Energy-Efficiency (OEE) from the users' perspective. Firstly, a realistic and new model of the Long-Term Evolution (LTE) user device's power consumption is proposed. This model includes the power used for operating modes and signal processing of mobile devices during the uplink data transmission. Secondly, the optimization problem is formulated by maximizing the OEE function subject to each user's quality-of-service (QoS) and a power constraint. Then, we allocate online power for the OEE by jointly optimizing the Macro Base Station (MBS) users and small cell Radio Remote Heads (RRHs) users. Furthermore, the Online Frank-Wolfe (OFW) method is adopted to obtain the optimal solution for the formulated OEE optimization problem. The regret metric is derived to describe the performance of the OFW. Finally, numerical results validate the accuracy of the proposed power model and demonstrate the superiority of the proposed method compared to the benchmark.