Heterogeneous Small Cell Network (HSCN) will have wide application given its ability to improve system capacity and hot spot coverage. In order to increase the efficiency of spectrum and energy, a great deal of research has been carried out on radio resource management in HSCN. However, it is a remarkable fact that the user experience in terms of traffic rate demands has been neglected in existing research with excessive concentration on network capacity and energy efficiency. In this paper, we redefined the energy efficiency (EE) and formulate the joint optimization problem of user experience and energy efficiency maximization into a mixed integer non-linear programming (MINLP) problem. After reformulating the optimization problem, the joint subchannel (SC) allocation and power control algorithm is proposed with the help of cluster method and genetic algorithm. Simulation results show that the joint SC allocation and power control algorithm proposed has better performance in terms of user experience and energy consumption than existing algorithms.

Network traffic forecasts, as it is well known, can be useful for network resource optimization. In order to minimize the forecast error by maximizing information utilization with low complexity, this paper concerns the difference of traffic trends at large time scales and fits a dual-related model to predict it. First, by analyzing traffic trends based on user behavior, we find both hour-to-hour and day-to-day patterns, which means that models based on either of the single trends are unable to offer precise predictions. Then, a prediction method with the consideration of both daily and hourly traffic patterns, called the dual-related forecasting method, is proposed. Finally, the correlation for traffic data is analyzed based on model parameters. Simulation results demonstrate the proposed model is more effective in reducing forecasting error than other models.