A new multi-carrier M-ary differential chaos shift keying system with code index modulation, referred to as CIMMC-M-DCSK, is proposed in this paper. In the proposed CIMMC-M-DCSK system, the reference and information-bearing signals for each subcarrier can be transmitted simultaneously by using the orthogonal sinusoidal carriers, where the information-bearing signal adopts the M-DCSK modulation to further increase the data rate. With an aim to making full use of the system energy resources, the reference signals in all subcarriers are coded by a Walsh code to carry additional information bits. The analytical bit-error-rate (BER) expressions of the proposed CIMMC-M-DCSK system are derived over additive white Gaussian noise (AWGN) as well as multipath Rayleigh fading channels. Furthermore, a noise-reduction scheme and a hierarchical-modulation scheme are designed for the proposed system. In particular, the former scheme can significantly improve the BER performance while the latter scheme can provide different quality of service (QoS) for the transmitted bits according to their different levels of importance. Simulation results verify the accuracy of the analytical expressions and the superiority of the proposed systems.

This paper proposes a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided wireless-powered non-orthogonal multiple access (NOMA) system, which includes an access point (AP), a STAR-RIS, and two non-orthogonal users located at both sides of the STAR-RIS. In this system, the users first harvest the radio-frequency energy from the AP in the downlink, then adopt the harvested energy to transmit information to the AP in the uplink concurrently. Two policies are considered for the proposed system. The first one assumes that the time-switching protocol is used in the downlink while the energy-splitting protocol is adopted in the uplink, which is referred to as time-switching and energy-splitting policy (TEP). The second one assumes that the energy-splitting protocol is utilized in both the downlink and uplink, which is referred to as double energy-splitting policy (EEP). The outage probability, sum throughput, and average age of information (AoI) of the proposed system with TEP and EEP are investigated over Nakagami- m fading channels. In addition, we also analyze the outage probability, sum throughput, and average AoI of the STAR-RIS aided wireless-powered time-division-multiple-access (TDMA) system. Simulation and numerical results demonstrate that the proposed system with TEP and EEP yields better performance than baseline schemes. Although the proposed system sacrifices the outage probability and average AoI performance compared to STAR-RIS aided wireless-powered TDMA systems, it significantly enhances the sum throughput. Furthermore, we design a genetic-algorithm based time allocation and power allocation (GA-TAPA) algorithm to maximize the sum throughput and ensure a certain average AoI. Simulation results indicate that the proposed GA-TAPA algorithm can further improve the sum throughput of the proposed system.

LoRa is a modulation technology for low power wide area networks (LPWAN) with enormous potential in 5G era. However, the performance of LoRa system deteriorates seriously in fading-channel environments. To tackle this problem, in this paper we introduce multiple-input-multiple-output (MIMO) configuration employing space-time block coding (STBC) schemes into the LoRa system to formulate an STBC-MIMO LoRa system. Then, we investigate the theoretical performance of the proposed system over Rayleigh fading channels. To this end, we derive the distribution of the decision metric for the demodulator in the proposed system. Based on the above distribution, we propose a closed-form approximate BER expression of the proposed system when perfect and imperfect channel state information (CSI) are considered. Furthermore, we analyze the diversity order and the throughput of the proposed system. The results of the diversity analysis demonstrate that the diversity order of the system in the imperfect CSI scenario with fixed channel estimate error variance is zero. However, in the imperfect CSI scenario with a decreasing channel estimate error variance and the perfect CSI scenario, the system can achieve full diversity. In addition, the results of the throughput analysis show that the throughput of the proposed system is little affected by CSI conditions. Simulation results verify the accuracy of the theoretical analysis and the excellent performance of the proposed system. Due to such superiority, the proposed STBC-MIMO LoRa system can be considered as a good scheme for LPWAN.

Objective: To develop an objective and efficient method to automatically identify Parkinson’s disease (PD) and healthy control (HC). Methods: We design a novel model based on residual network (ResNet) architecture, named PD-ResNet, to learn the gait differences between PD and HC and between PD with different severity levels. Specifically, a polynomial elevated dimensions technique is applied to increase the dimensions of the input gait features; then, the processed data is transformed into a 3-dimensional picture as the input of PD-ResNet. The synthetic minority over-sampling technique (SMOTE), data augmentation, and early stopping technologies are adopted to improve the generalization ability. To further enhance the classification performance, a new loss function, named improved focal loss function, is developed to focus on the train of PD-ResNet on the hard samples and to discard the abnormal samples. Results: The experiments on the clinical gait dataset show that our proposed model achieves excellent performance with an accuracy of 95.51%, a precision of 94.44%, a recall of 96.59%, a specificity of 94.44%, and an F1-score of 95.50%. Moreover, the accuracy, precision, recall, specificity, and F1-score for the classification of early PD and HC are 92.03%, 94.20%, 90.28%, 93.94%, and 92.20%, respectively. Furthermore, the accuracy, precision, recall, specificity, and F1-score for the classification of PD with different severity levels are 92.03%, 94.29%, 90.41%, 93.85%, and 92.31%, respectively. Conclusion: Our proposed method shows better performance than the traditional machine learning and deep learning methods. Clinical impact: The experimental results show that the proposed method is clinically meaningful for the objective assessment of gait motor impairment for PD patients.