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.

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.

Massive multiple-input-multiple-output (MMIMO) in the mmWave band is an essential technique to achieve the desired performance for 5G new radio (NR) systems. To employ mmWave MMIMO technology, an important challenge is maintaining seamless mobility to users because we need to consider beam-switching within a cell besides the handover between cells. For mobility management in 5G NR systems, 3GPP specified a beam-level-mobility scheme that includes beam pairing and maintenance between a transmitter (Tx) and receiver (Rx) pair. We propose a unific-measurement report based mobility management scheme for improved radio-link-failure (RLF) rate and the accuracy of the Tx-Rx-beam-pair (TRP) selection with low overhead in 5G mmWave MMIMO networks where both handover and beam-switching are required. Furthermore, we modeled a finite-state-machine (FSM) for a user terminal to evaluate performance gain based on a system-level-simulation (SLS). We use the FSM-based Monte-Carlo SLS for the experiment and compare the performance of the proposed scheme with that of existing schemes in the scenario where both beam and cell-level-mobility are necessary. We show that the proposed scheme achieves an improvement in terms of the 3-dB loss probabilities representing the accuracy of the TRP selection, signal-to-interference-and-noise-ratio (SINR), and RLF rates with a lower signaling overhead compared to existing methods.

Estimating the number of competing terminals n (who wish to transmit a packet at the same time) in the IEEE 802.11 system is important for system throughput performance because optimal back-off window size needs to be selected based on n. Therefore, as a new approach for estimating n, we propose H infinity filter that does not need a state variation detector as opposed to the cases of previously proposed approaches. The state variation detector’s flaw is incurring tracking latency in addition to the side effect of increased computational cost. All previously proposed approaches demand the employment of the state variation detector to detect the variation of n in the IEEE 802.11 system. By employing H infinity filter, we show improved throughput performance of the system compared to that of previously proposed approaches (e.g., the Kalman filter and particle filter) based on the improved performance in tracking n. In this paper, we justify the superiority of the proposed approach in the terms of tracking performance, throughput performance, and computational complexity.