Future network infrastructures will become more complex, which will require fast and secure service delivery in unpredictable scenarios, including diverse devices and multiple 5G/6G access lines supported by different carriers. In addition, future carrier networks are expected to adopt network disaggregation technologies that integrate superior technologies from different vendors, which are often “black-boxed”, to meet specific service requirements. We define a “black-boxed” node as a network node where the internal implementation of packet processing mechanisms is not disclosed, although hardware specifications are known, as seen in vendor products. This poses a challenge in the performance verification of network nodes and components for black-boxed network nodes. Consequently, a research issue emerges: the need to highly accurately estimate the performance of black-boxed network nodes in advance, where it is difficult to estimate the per-packet cost of how much bandwidth and computation time for a single packet consumes in the face of unexperienced scenarios. Therefore, the objective of this research is to explore the potential for digitally verifying the performance of black-boxed network nodes, focusing on refining the accuracy of extrapolation for their metrics. This extrapolation utilizes available external factors, including measured target metrics, node settings, and traffic conditions. In response, we propose a node modeling method that is a combination of neural processes, a type of meta-learner. The novelty of the proposed algorithm lies in its approach to iteratively append inferred router metrics to the training datasets based on feature importance. Experimental results demonstrate that by including router settings and inferred other router metrics in the training dataset based on software routers, the coefficient of determination for inferred router metrics; packet loss rates, throughput, and packet delays in the extrapolation domain surpasses the results obtained from the original training dataset alone.

In a space-based automatic identification system (AIS), a satellite has a wide coverage area and thus can receive AIS signals from ships in the high seas. However, wide coverage can cause multiple AIS packets to collide with each other at the satellite receiver. Furthermore, transmitted packets are affected by channel parameters, such as Doppler shifts, channel impulse response, and propagation delay time, which are remarkably different in each packet because these parameters depend on the distance and relative speed between ships and the satellite. Therefore, these parameters should be estimated and used for multiuser detection to detect collided packets separately. Nevertheless, when the received power difference between packets or desired to undesired signal power ratio (DUR) is low, the accuracy of the channel parameter estimation is degraded so severely, such that multiuser detection cannot maintain sufficient bit error rate (BER) performance. To compensate for the reduced accuracy, this paper proposes a highly accurate channel estimation method. In addition to the conventional correlation-based channel estimation, the proposed method applies the quasi-Newton and least-squares methods to estimate Doppler frequency and channel impulse response, respectively. Regarding the propagation delay time, the conventional correlation-based channel estimation is repeated for improvement. Multiuser detection based on the Viterbi algorithm is performed using the estimated channel parameters. Computer simulations were conducted under the conditions of two collision packets and Rician fading, and the results show that the proposed method can significantly improve the accuracy of the channel estimation and BER performance than the conventional method.

This paper presents the miss-detection probability (MDP) of the Physical Random Access Channel (PRACH) with a short sequence for the 3GPP New Radio specifications in the presence of carrier frequency offset (CFO) in millimeter-wave bands. At a base-station receiver, the correlation of every repetition unit of the PRACH-preamble sequence between the received PRACH signal and the PRACH-preamble sequence candidates is computed using a matched filter in the frequency domain. This is followed by combining the correlations of the repeated PRACH-preamble sequences that correspond to the fast Fourier transform blocks in the time domain. The multiple correlations of the repeated PRACH sequences are combined by coherent combining with in-phase and quadrature components or by combining in squared form in the power domain, followed by the detection of the sequence and received timing of the desired PRACH. This paper first investigates the effect of the repetition of the PRACH-preamble sequence on reducing the MDP for various 3GPP Tapped Delay Line channel models in non-line-of-sight (NLOS) and LOS environments. Next, we establish the best combining method for the correlations of the repeated PRACH sequences from two candidates based on the PRACH MDP for various types of PRACH formats and for various subcarrier spacings (SCSs) from 120 kHz to 960 kHz in the presence of CFO based on extensive simulations. We also show that a wide SCS of up to 960 kHz is effective in reducing the PRACH MDP in the presence of CFO for the frequency stability of a set of user equipment of up to 3 ppm at the carrier frequency of 60 GHz.

The signal levels of Ku-band BS broadcast radio wave and JCSAT-5A beacon radio wave have been simultaneously measured at Osaka Electro-Communication University (OECU, Neyagawa, Osaka), NTT Yokosuka R&D Center (Yokosuka, Kanagawa), and satellite base station (Matsuyama, Ehime), respectively, from April 2022 to March 2023. The yearly cumulative distribution of rain attenuation at Yokosuka station shows the same increasing tendency compared to the ITU-R recommendations, as at Neyagawa station, while the increasing tendency is not clear at Matsuyama station. Also, site diversity techniques are examined among these three stations with relatively long distances of about 300-700 km. The site diversity effects among the three stations are almost consistent with the ITU-R recommendations between eastern and western areas of Japan. The 99.9% annual available time (0.1% unavailable time) percentage of satellite operations is shown to be guaranteed by the rain margins of 3-5 dB for the yearly rain attenuation statistics at the three stations. The monthly rain attenuation statistics, however, indicate that the rain margins of 6-10 dB are required to maintain the same 99.9% available time percentage primarily around summer time. The increase in rain margins is successfully suppressed under 3 dB using the site diversity operations. This increase in rain margins is well explained by the worst month statistics of the ITU-R recommendations.

In this paper, a Doppler-tolerant waveform is proposed as a transmitting signal for joint radar and communication systems. In the proposed waveform, communication signals are multiplexed at the side band of a linear frequency modulated (LFM) pulse, based on the orthogonal frequency division multiplexing (OFDM) scheme. Therefore, the proposed waveform can maintain Doppler-tolerance in radar use as well as the original LFM pulse can. In addition, it is also capable of flexibly increasing the transmission rate in communication use by assigning more communication signals at the side-band subcarriers. Numerical simulations were carried out to comprehensively examine the proposed waveform in terms of the probability of detection in radar use and the symbol error rate in communication use. In conclusion, the proposed waveform is suited to the transmitting signal for joint radar and communication systems, especially with maintaining Doppler-tolerance to detect fast-moving targets.

The electrocardiogram (ECG) signals P-wave, QRS wave and T-wave all reflect the activity of the heart, and the analysis of ECG signals can provide basic information for the diagnosis and prevention of heart disease. In the work of this paper, frequency-modulated continuous-wave (FMCW) radar and deep learning network are utilized to acquire ECG signals non-contactly, and we propose an improved differential and cross multiply (DACM) algorithm and a multi-neighbor differentiator for extracting cardiac motion acceleration information, as well as a partitioned reconstruction network incorporating an attention mechanism of encoder-decoder to achieve ECG signal reconstruction. The design principle is a combination of signal segmentation and deep learning (Sequence-to-sequence and attention) called SS-S2SA. firstly, a segmentation algorithm is applied to segment the acceleration signal and the ECG signal synchronously, and then the cardiac motion acceleration signal is mapped to the ECG signal using the SS-S2SA network. The method proposed in this paper is demonstrated to reconstruct ECG signals more accurately and finely by training more than 18,000 acceleration signal segments from 10 healthy subjects and evaluating the predictions from 5 subjects. The average correlation coefficient between the predicted signal and the real signal is about 0.92, and the mean absolute error (MAE) of the timing of the P-peak, R-peak, and T-peak are 13.9 ms, 8.1 ms, and 11.1 ms, respectively.

Received Signal Strength Indicator (RSSI)-based localization is of interest in indoor localization systems. In this study, we propose a method to improve localization accuracy using interference-oriented fluctuation. We estimate the distance between target and beacon nodes by utilizing the nodes located around them. When the beacon node transmits a signal to the target for measuring the distance, the surrounding nodes also transmit a copy of the signal. Such signals cause interference patterns at the beacon, thereby randomizing the RSSI. Our developed statistical signal processing enables the estimation of the strength of the received signal with the randomized RSSI. We numerically show that the distance between the target and beacon nodes is estimated with lower error than when using the conventional method. In addition, such accurate distance estimation allows significant improvement in localization performance. Our approach is useful for indoor localization systems, for example, those in medical and industrial applications.