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In this paper, we introduce an uplink transmission model with mobile users, where each user maximizes his/her utility to achieve the best performance. We propose a power allocation scheme for each mobile user when all channel information is available. Moreover, we illustrate that one user would expect to predict the aggregate interference to maximize the utility when the channel information is incomplete. It is shown that this approach forms a game with incomplete information. We demonstrate the prediction rules that can help the mobile users dynamically adjust predictions and apply the Kalman filter to tackle measurement noises. We also illustrate the theoretical bound for the difference between the utility with prediction and that with complete information. Moreover, applying dynamic programming from control theory, we give a dynamic power allocation scheme based on the predictions. Simulation results indicate that our power allocation scheme with complete information and dynamic power allocation with predictions give better performance compared with the scheme with even power allocation. In addition, results under our dynamic power allocation scheme are close to those under the power allocation with complete information.