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.