We propose approaches to the problem of multi-paths beam tracking in millimeter wave (mmWave) mobile communications. Precise angle tracking is necessary to take advantage of beamforming technology in this mmWave band. The mobile channel is a highly nonlinear function with respect to the state that comprises the beam angle and the channel path gain. Therefore, the beam angle tracking is challenging and crucial. We propose a couple of particle filtering frameworks for beam tracking, and show outperforming result of auxiliary particle filtering approach over recently proposed tracking approaches.

In this paper, we propose a new approach for tracking a target maneuvering based on the multiple-constant-turns model. Usually, the interactive-multiple-model (IMM) algorithm based on the extended Kalman filter (EKF) is employed for this problem with successful tracking performance. Recently proposed IMM-particle filter (PF) shows outperforming results for this nonlinear problem. The proposed approach is a new framework of particle filtering (PF) that adopts the minimax strategy to standard PF. The minimax strategy results in the decreased variance of the weights of particles that provides the robustness against degeneracy phenomenon that is a common problem of generic particle filtering. In this paper, we show outperforming results by IMM-MPF over IMM-PF besides the IMM-EKF in terms of various aspects.

We propose a new approach for maximizing system throughput while simultaneously obtaining optimal-coverage in terms of minimal radio link failure (RLF) rates in 5G heterogeneous ultra-dense networks (HUDN). To overcome the trade-off issue between the system throughput maximization and the coverage optimization where we can hardly achieve both optimality together, we employ a joint method of “iteration and convex optimization based power control (PC),” and “advanced range expansion (RE) technique.” This algorithm works via a self organizing networks (SON) module, and is compatible with the radio protocol layer defined in specifications. In experimental simulations, we could achieve compositive optimality with considerable performance gain by the proposed approach over existing methods.