In contrast to the 4G LTE, one of the key requirements of 5G and beyond networks is ultra-high reliability. Towards this end, we investigate the problem of scheduling with joint power allocation for multi-user multiple-input multiple-output (MU-MIMO) systems by taking the reliability into account. In this study, two novel MU-MIMO scheduling methods are proposed based on the use of bit error rate (BER) as the scheduling metric; however, capacity has been typically considered as an user-selection metric in MU-MIMO systems to increase the sum-throughput. In particular, we also develop a new power allocation strategy that can minimize the BER for a given data transmission rate. The computational complexities of the two aforementioned BER-based scheduling algorithms, as well as the conventional capacity-based algorithm, are analyzed. Our simulation results show that the proposed BER-based scheduling algorithms with the joint power allocation have better performance in terms of BER compared to the conventional MIMO scheduling schemes such as a single-user MIMO (SU-MIMO) and the capacity-based scheduling algorithm. The proposed multi-user scheduling framework can be utilized in various emerging applications demanding higher reliability and availability such as mission-critical services.

Hybrid cryptographic protocol was proposed by integrating quantum-safe algorithms (Kyber and Dilithium) together with a classical ECDSA-based signature scheme to verify secure proof on blockchain. Since quantum computers pose some loopholes in existing cryptography algorithms, the proposed mechanism addresses those vulnerabilities by maintaining compatibility with the existing infrastructure. The protocol integrates post-quantum key exchange to derive shared secrets securely. The shared secrets are used to enable hybrid encryption for efficient data protection. Additionally, the implementation uses a dual-signature approach, using quantum safe and classical signatures. These signatures are combined to ensure robust and tamper-resistant proof verification. The deployed solution validates its feasibility and efficiency for smart contract-based verification. The protocol maintains backward compatibility with existing systems while providing strong resistance to quantum attacks. Benchmark results demonstrate the system’s scalability and robustness under high transaction loads, with efficient gas usage for on-chain proof validation. This approach paves the way for post-quantum secure applications, including verifiable proofs, secure key exchanges, and decentralized systems resilient to quantum adversaries.

Rail breaks, which are crucial maintenance issues for the railways, require immediate inspection and maintenance as it can cause severe railway accidents. Though, it is still difficult to promptly detect rail breaks that occur during train operation even with the advances in maintenance and inspection technologies. In this research, as a preliminary study on rail break detection system, a deep learning‐based discontinuous rail position classification method, which is using vibration data obtained from distributed acoustic sensing (DAS) system during train operation, is proposed. To analyze the vibration data, a preprocessing algorithm for determining train occupancy is applied first. After that, the data in the space–time domain occupied by the train is converted to the spectrogram which is in the frequency domain by using short‐time Fourier transformation (STFT). In the third step, the spectrogram images are applied to the proposed 2D convolutional neural network (2D CNN) model and the network detects discontinuous rail positions along the track, which are geometrically distinct from continuous welded rails, such as rail breaks. In order to evaluate the superiority of the proposed network model, performance comparison tests with other existing models were conducted with data collected from an actual railway line. From the results, the proposed model could achieve 99.17%, 93.33%, 87.5%, 90.32% for accuracy, precision, recall, and F1 score, respectively, and the results show overwhelming detection performance compared to other models.

This correspondence is a response to Comments on the above article raised by Wen et al. (2023). The authors in Wen et al. (2023) insist that there might be some errors in the Kalman filter derivation procedure, mathematical expression, and computability of high-order error terms in Ko et al. (2022). However, the arguments are not always true. The authors in Wen et al. (2023) misunderstood the derivation of the Kalman filter by the assumption of an unbiased estimator. In addition, they also misunderstood the assumption of noise distribution for the Kalman filter design. In this correspondence, we analyze and refute the comments in a pointby-point fashion.

Cooperative hybrid-ARQ protocols have been widely studied because the efficiency of time resources of them is higher than that of cooperative protocols. In this paper, a cooperative hybrid-ARQ protocol based on incremental redundancy hybrid-ARQ technique is proposed and analyzed with respect to the relay location. Based on the analysis, the optimum initial rate is searched and the maximum throughput is obtained with respect to the relay location.

This study analyzes the performance of positioning techniques based on configuration changes of 5G New Radio (NR) signals. In 5G networks, a terminal’s position is determined from the Time of Arrival (TOA) of Positioning Reference Signals (PRS) transmitted by base stations. We propose an algorithm that improves TOA accuracy under low sampling-rate constraints and implement 5G PRS for positioning in a software-defined modem. We also examine how flexible time–frequency resource allocation of PRS affects TOA estimation accuracy and discuss optimal PRS configurations for a given signal environment. With the proposed configuration, the terminal can achieve sub-sample TOA accuracy, reaching approximately 0.3 samples.