Human body area communication has been widely used in the fifth-generation (5G) wireless communication systems. Human bodies bring complexity for channel modeling, and directly affect the performance of the wireless communication systems. In this letter, channel measurements, simulations and modeling are carried out in a curved tunnel scenario with several human bodies. Human body is modeled by two typical methods to analyze the influence of the channel modeling on the accuracy based on measurements and the innovative ray-tracing method. The comparison of path loss models is provided and a novel generalized path loss model in term of the curved tunnel scenario is proposed. Multipath dispersion properties are characterized and corresponding statistical distributions are also investigated. Furthermore, the multipath components parameters including angular characteristics of angle of arrival (AoA) and power of multipath components are analyzed. Multipath clustering is identified using the multipath component distance (MCD).

Propagation measurements of wireless channels performed in the tunnel environments at 6 GHz are presented in this paper. Propagation characteristics are simulated and analyzed based on the method of shooting and bouncing ray tracing/image (SBR/IM). A good agreement is achieved between the measured results and simulated results, so the correctness of SBR/IM method has been validated. The measured results and simulated results are analyzed in terms of path loss models, received power, root mean square (RMS) delay spread, Ricean K-factor, and angle of arrival (AOA). The omnidirectional path loss models are characterized based on close-in (CI) free-space reference distance model and the alpha-beta-gamma (ABG) model. Path loss exponents (PLEs) are 1.50–1.74 in line-of-sight (LOS) scenarios and 2.18–2.20 in non-line-of-sight (NLOS) scenarios. Results show that CI model with the reference distance of 1 m provides more accuracy and stability in tunnel scenarios. The RMS delay spread values vary between 2.77 ns and 18.76 ns. Specially, the Poisson distribution best fits the measured data of RMS delay spreads for LOS scenarios and the Gaussian distribution best fits the measured data of RMS delay spreads for NLOS scenarios. Moreover, the normal distribution provides good fits to the Ricean K-factor. The analysis of the abovementioned results from channel measurements and simulations may be utilized for the design of wireless communications of future 5G radio systems at 6 GHz.

For unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) systems, a semantic-based channel state information (CSI) feedback scheme is proposed in this paper. Unlike traditional full CSI feedback, the proposed scheme minimizes feedback burden by utilizing predefined semantic databases at both the transmitter and receiver. Firstly, a deep learning-based clustering method is developed to construct the semantic database from measured CSI samples. Then, an incremental clustering-based identification method is proposed, enabling dynamic updates and adjustments to semantic databases as new CSI is continuously acquired. Finally, the proposed CSI feedback scheme is validated through scenario identification, and extensive channel measurements are conducted in three typical campus scenarios: playground, lake, and buildings. The results show that the accuracy of the semantic feedback-based scenario identification reaches 97.5%, which is 0.6% higher than the accuracy of the full-CSI feedback-based scenario identification. Specifically, the CSI is fed back through semantic database labels, requiring only a few bytes. This significantly reduces feedback burden while maintaining high accuracy of ISAC tasks. Furthermore, the proposed feedback scheme can also be extended to other UAV-assisted applications, such as the Internet of Things and emergency response.

Epileptic seizure is one of the most common neurological disorders characterized by sudden abnormal discharge of neurons in the brain. Automated seizure detection using electroencephalograph (EEG) recordings would improve the quality of treatment and reduce medical overhead. The purpose of this paper is to design an automated seizure detection framework that can effectively identify seizure and non-seizure events by discovering connectivity between brain regions. In this work, a weighted directed graph-based method with effective brain connectivity (EBC) is proposed for seizure detection. The weighted directed graph is built by analyzing the correlation among the different regions of the brain. Then, graph theory-based measures are used to extract features for classification. Furthermore, we illustrate the ability of the proposed method to achieve seizure detection for the patient-specific model and the cross-patient model. The results show that the proposed method achieves accuracy values of 99.97% and 98.29% for the patient-specific model and the cross-patient model in the CHB-MIT dataset, respectively. These results demonstrate that the proposed method achieves an effective classification performance and can be used to provide assistance for automatic seizure detection and clinical diagnosis.

Epilepsy, a neurological disease associated with seizures, affects the normal behavior of human beings. The unpredictability of epileptic seizures has caused great obstacles to the treatment of the disease. The automatic seizure detection method based on electroencephalogram (EEG) can assist experts in predicting seizures to improve treatment efficiency. Epileptic seizure detection cannot be achieved accurately using the single-view characteristics of the signals. Moreover, manual feature extraction is a time-consuming task. To design a high-performance seizure identification method, automatic learning of multi-view features becomes an indispensable part for seizure detection. Therefore, the paper proposes a multi-input deep feature learning networks (MDFLN) model, which comprehensively considers the features from the time domain and the time–frequency (TF) domain for EEG signals. The MDFLN model automatically extracts the feature information of the signals through deep learning networks. Then, the bidirectional long short-term memory (BLSTM) network is used to distinguish seizure and nonseizure events. Furthermore, the effectiveness of the proposed network structure is verified in two public datasets. The experimental results demonstrate that the classification accuracy of the proposed method based on multi-view features is at least 2.2% higher than the single-view features. The MDFLN achieves better performance on CHB-MIT and Bonn datasets with accuracy of 98.09% and 98.4%, respectively. The fine-tuned model with the validation set also improves the classification performance. Compare with the state-of-the-art seizure detection methods, the multi-input deep learning network has superior competence with high sensitivity on the CHB-MIT dataset. The proposed automatic seizure detection method can reduce time consumption and effectively assist experts in the clinical diagnosis and treatment.

The millimeter-wave (mmWave) and massive multiple-input multiple-output (MIMO) wireless communication technologies provide vital means to resolve many technical challenges of the fifth-generation (5G) or beyond 5G (B5G) network. Analyzing the measured datasets extracted from the channel measurements can provide insight into the characteristics of radio channels in different scenarios. Therefore, mmWave massive MIMO channel measurements, simulation, and modeling are carried out in the high-speed railway waiting hall environments at 28GHz. The multipath components (MPCs) parameters are estimated for line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios based on the space-alternating generalized expectation-maximization (SAGE) algorithm. Delay spread (DS), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) are analyzed. And they are processed by using the K-mean algorithm. In addition, propagation characteristics are simulated based on the improved ray tracing method of shooting and bouncing ray tracing/image (SBR/IM). The correctness of the improved ray tracing method is verified by comparing the measured results with the simulated results. The large-scale path loss (PL) is characterized based on close-in (CI) free-space reference distance model and the floating-intercept (FI) path loss model. Furthermore, statistical distributions for root-mean-square delay spread (RMS DS) are investigated. The Gaussian distribution best fits the measured data of RMS delay spread. Finally, multipath clustering is identified using the multipath component distance (MCD). The analysis of these results from mmWave massive MIMO channel measurements and simulation may be instructive for the deployment of the 5G or B5G wireless communications systems at 28GHz.