In this paper, the push-pull converter with asymmetrical operation is investigated for applying wide input voltage range. A switch is driven by asymmetrical gate waveforms in the investigated operation, and the primary side of the push-pull converter is configured two conversion circuits. Therefore, two different conversion ratios can be obtained by different numbers of turns in each circuit, and this method enables to handle the wide input voltage range. The operation principle and steady state characteristics are analytically and experimentally examined in this paper. As a result, the operating principle of the converter with asymmetrical operation has been analytically clarified, and it has been experimentally confirmed. Moreover, the operation limit of this converter has also been analytically and experimentally confirmed. The output voltage can be regulated 48 V for all input voltage range of 100 to 400 V, and the maximum efficiency is achieved 90%in low input voltage range.

This paper describes a modeling method for rapid computation of input-output voltage conversion gains of an LLC converter with high accuracy. This method quickly predicts the gain close to that of an actual circuit, even when circuit component values, switching frequency, and load conditions vary. Conventionally, the Fundamental Harmonic Approximation (FHA) method calculates the gain rapidly thanks to a simple equation. However, since FHA relies on an approximation, its accuracy decreases as an operating frequency deviates from the series resonant frequency. To address this issue, this study employs an Artificial Neural Network (ANN), a type of machine learning (ML), to estimate prediction errors and combines them with the gain obtained from FHA. This paper presents a procedure for dataset creation, an ANN training process, and a method for synthesizing the gain. In this paper, circuit simulation results of an LLC converter with a half-bridge circuit are considered as reference values (= ground truth). Test results demonstrate that selecting appropriate features for the input and output layers of the ANN decreases the prediction error.

In general residential and non-residential spaces, such as houses, offices and shops, lighting equipment is powered from AC power. This requires an isolated AC-DC power converter in each lighting set, which not only limits the shape and design of the equipment but also causes non-negligible power loss. In this study, the authors investigated the distribution of 24 V low-voltage DC power to residential and non-residential spaces, which has been gaining popularity in recent years due to its compatibility with renewable energy. The authors proposed and developed an ultra-thin card-shaped driver circuit for LED lighting. Experimental results verified that the developed 2 mm thin LED driver achieves high power conversion efficiency even in very high frequency quasi-soft switching operation up to 2.0 MHz in a practical load range.

In scenarios of 6G wireless communication systems, UAV-assisted covert communication can significantly enhance system performance due to its flexible deployment capabilities. However, existing researches often overlook the impact of ground obstacles on air-to-ground (A2G) channels and the optimal placement of UAVs, resulting in suboptimal communication quality for covert communication. To address this gap, we propose an obstacle-assisted UAV covert communication joint optimization method (OA-UCCJOM), optimizing the UAV’s position and transmit power with the influence of obstacles considered, to maximize communication quality between the UAV (Alice) and a legitimate ground receiver (Bob). Specifically, we account for the uneven distribution of obstacles around a ground warden (Willie) and categorize the UAV’s spatial positions into a Base Scenario and five practical scenarios based on common operating contexts. In each two-dimensional vertical plane determined by Willie, we narrow down the feasible flight region for the UAV’s optimal placement and solve the joint optimization problem in certain cases. The simulation results verify the geometric conclusions, show the advancement of our proposed OA-UCCJOM, and demonstrate that the UAV can achieve near-optimal performance at certain positions.

In large-scale RDMA networks, packet drops become inevitable and frequent due to congestion or failure. Since the original Go-Back-N (GBN) loss recovery implementation on RDMA NICs exhibits noticeable performance degradation even at low packet loss rates, adopting selective retransmission on RDMA NICs has been proven efficient. However, existing efforts have primarily focused on the efficiency of selective retransmission in single-path transmissions, neglecting challenges posed by multi-path scenarios, such as the ambiguity between packet losses and out-of-order arrivals, the ignorance and incompetence of retransmit path selection, etc. These oversights may lead to long retransmission time or even retransmission failure, potentially decreasing the throughput. Therefore, to enable fast and reliable loss recovery in multi-path RDMA transport, we propose OrderRE, an out-of-order degree based redundant retransmission scheme. In OrderRE, based on the out-of-order degree of arriving packets, the receiver can recognize binary path states (fast or slow) and distinguish packet losses from out-of-order arrivals. Further, based on a multicast-based mechanism implemented via programmable switches, the retransmitted packets can be duplicated and redirected to a group of fast paths identified by the receiver, achieving path-restricted redundant retransmission without path state maintenance on RDMA NICs. Finally, we integrate OrderRE into existing state-of-the-art multi-path RDMA protocol MP-RDMA and conduct various evaluations. It can be shown that when compared to naive selective retransmission, OrderRE has achieved an average of 30% improvement in retransmission speed and a more than 90% reduction in retransmission failures. Consequently, OrderRE can achieve approximately 2× the goodput of the naive scheme under the minimum bitmap size to sustain goodput, when the packet loss rate is 1%.

The quality and coverage of signals within cellular networks are determined by base stations and their locations. The deployment of a base station relies on fine-grained radio maps. However, previous work involves iterating through all possible base station locations and selecting the base station location with the maximum signal coverage as the optimal reference deployment location, resulting in repeated and large time-consuming tasks. This paper proposes a Visual Transformer (ViT) based radio map estimation method, termed TxSTrans (Transmitter Selection Transformer), unifying radio map estimation and optimized site selection. The TxSTrans can accept parallel base station data inputs and quickly recommend the optimal base station site within given candidates to maximize the signal coverage. TxSTrans contains a multi-modal feature fusion module for integrating building maps (environment) and given base station coordinates, an information fusion pyramid module for reconstructing a high-resolution radio map, and a recommendation module for finding the optimal base station site among candidates. A knowledge distillation learning method is also incorporated to train the parameters of the recommendation module to sense the best base station site among candidates. Simulation results show that TxSTrans facilitates radio map estimation and optimized site selection. The runtime of the base station siting is 4.62 times faster than the sequential search for the optimal base station site by RadioUnet.

Network-Assisted Full-Duplex (NAFD) cell-free massive multiple-input multiple-output (CF-mMIMO) systems can improve the spectral efficiency (SE) by dynamically allocating the numbers of uplink (UL) and downlink (DL) remote antenna units (RAUs) to realize simultaneous UL and DL transmission without self-interference (SI). However, with the promotion of the green communication concept and energy efficiency (EE) becoming an important performance metric, the EE of the NAFD system needs further evaluation and optimization under the realistic energy consumption model. In this paper, a practical energy consumption model for the NAFD systems is established, and its advantages in terms of EE are highlighted compared with the existing Co-frequency Co-time Full Duplex (CCFD) systems. A Q-learning-based duplex mode optimization algorithm is proposed to improve system EE. Simulation results show that the proposed mode optimization algorithm can achieve higher EE compared with existing mode selection schemes.

This paper proposes a scalable user scheduling method for cell-free multi-input multi-output (MIMO) networks with user-centric transmission reception point (TRP) clustering. The proposed three-step scheduling method relies on some level of inter-TRP information exchange to manage the user-centric TRP clustering. However, the geographical range of the inter-TRP information exchange is limited to that of the user-centric TRP clustering (geographical distribution range of the TRP locations per cluster). The number of users considered in the scheduling process at each TRP is finite even when the total number of users in the system grows infinitely as the system coverage area expands. Therefore, the proposed method ensures scalability and is easy to apply to large-scale systems. Furthermore, the framework of the proposed user scheduling protocol allows for the application of various scheduling metrics. Computer simulation results show that the proposed method achieves higher system-level throughput than that for the conventional method.

In the 3rd Generation Partnership Project (3GPP) New Radio (NR) radio interface, the physical random access channel (PRACH) is a physical channel that a set of user equipment (UE) transmits first asynchronously in uplink initial access. This paper presents the miss-detection probability (MDP) of the PRACH with a short sequence in the presence of asynchronous multi-access interference in the NR uplink. Multiple PRACH preamble sequences are generated using different root indices or different cyclic shifts (CSs) of the Zadoff-Chu sequence. The number of available CSs of the Zadoff-Chu sequence depends on the round trip delay time according to the distance from a cell site to the UE of interest and the delay time in multipath fading channels. Link-level simulation results show that when N_UE = 5 UEs transmit the PRACH simultaneously (N_UE denotes the number of simultaneously accessing UEs), the PRACH MDP with contention of the same PRACH sequence is degraded by approximately one order of magnitude compared to that without contention at the average received signal-to-noise ratio (SNR) of higher than approximately 0 dB for the cell radius of R_cell = 100 m in the 3GPP tapped delay line (TDL)-C channel model. We also show that the PRACH MDP of 1% is not achieved with contention for N_UE = 10 under identical conditions. Furthermore, we show that in the case with contention, no distinct difference is observed in the PRACH MDP according to the number of PRACHs for which the same PRACH sequence is selected. Finally, we show that approximately N_UE = 4, 4, and 2 UEs can satisfy the PRACH MDP of 1% for R_cell = 50 m, 100 m, and 200 m, respectively, in the TDL-C and E channel models.

The Frequency of Arrival (FOA) method is one way of achieving positioning using Low Earth Orbit (LEO) satellites. Positioning using LEO satellites can serve as an alternative to Global Navigation Satellite System (GNSS) in GNSS-denied environments. However, when using LEO satellites without accurate orbit information, FOA positioning accuracy is degraded by orbit errors. To address this issue, we propose a simple and effective method to correct these errors using a reference station. The reference station is installed at a known, static position and observes the same satellite as the user. By applying the correction data generated by the reference station, the user’s FOA positioning accuracy can be significantly improved. We evaluated this method using real data from both GPS and Iridium-NEXT satellites. When using GPS satellites, the positioning accuracy improved from 36 km to 3.5 km. Similarly, when using Iridium-NEXT satellites, the positioning accuracy improved from 27 km to 3.8 km.