In this paper, we propose a novel machine learning (ML) based link-to-system (L2S) mapping technique for inter-connecting a link-level simulator (LLS) and a system-level simulator (SLS). For validating the proposed technique, we utilized 5G K-Simulator, which was developed through a collaborative research project in Republic of Korea and includes LLS, SLS, and network-level simulator (NS). We first describe a general procedure of the L2S mapping methodology for 5G new radio (NR) systems, and then, we explain the proposed ML-based exponential effective signal-to-noise ratio (SNR) mapping (EESM) method with a deep neural network (DNN) regression algorithm. We compared the proposed ML-based EESM method with the conventional L2S mapping method. Through extensive simulation results, we show that the proposed ML-based L2S mapping technique yielded better prediction accuracy in regards to block error rate (BLER) while reducing the processing time.

In this paper, we propose a novel low-complexity multi-user superposition transmission (MUST) technique for 5G downlink networks, which allows multiple cell-edge users to be multiplexed with a single cell-center user. We call the proposed technique diversity-controlled MUST technique since the cell-center user enjoys the frequency diversity effect via signal repetition over multiple orthogonal frequency division multiplexing (OFDM) sub-carriers. We assume that a base station is equipped with a single antenna but users are equipped with multiple antennas. In addition, we assume that the quadrature phase shift keying (QPSK) modulation is used for users. We mathematically analyze the bit error rate (BER) of both cell-edge users and cell-center users, which is the first theoretical result in the literature to the best of our knowledge. The mathematical analysis is validated through extensive link-level simulations.

LTE/LTE-A systems have specified a discontinuous reception (DRX) mechanism for battery energy saving of user equipment (UE). Until now, most analytical works have been on the DRX mechanism based on a single packet server. However, a single packet server typically yields low performance in energy saving under high arrival rates. On the other hand, practical LTE/LTE-A systems can allocate a variable size of resource for UE at one service time. To reflect the variable size of resource allocation and analyze the exact DRX mechanism under high arrival rates, we first model the DRX mechanism based on a batch server with varying buffer thresholds and server capacities. We also investigate the effect of system parameters such as buffer threshold, server capacity, DRX cycle, and inactivity timer in terms of delay and sleep ratio. Then, we find the optimal system parameter values of the batch packet server based DRX mechanism while satisfying the given delay requirements and maximizing the sleep ratio simultaneously. Finally, we show that the batch packet server yields the longer sleep ratio from 0.6 to 0.9 at high arrival rates, compared to a single packet server.