Integrated sensing and communication enhances the spectral efficiency by using shared resources, eliminating the need for separate bandwidth allocations. Unmanned aerial vehicle (UAVs) play a key role in this, offering mobility, flexibility, and extended coverage for serving multiple users, especially in scenarios like disaster response and environmental monitoring. This paper explores multiple UAVs with integrated sensing and communication capabilities, using the Age of Information (AoI) metric to optimize resource allocation for timely data transmission. We propose two algorithms, Variable Particle Swarm Optimization (VPSO) and Twin Variable Neighborhood Particle Swarm Optimization (TVPSO), to jointly optimize power, bandwidth, and UAV trajectories to minimize AoI. Numerical results show the effects of the sensing and communication power ratio, the number of UAVs and the number of users on AoI and energy consumption. Furthermore, TVPSO is shown to outperform other PSO variants and the Deep Q Networks (DQN)-based approach, offering faster convergence and superior performance.

As living standards improve, the demand for health monitoring continues to rise. The daily continuous monitoring over the long term is still facing challenges due to limited technology advances with well body comfort. This article proposes a wireless nonintrusive sensing system based on a harmonic radar sensor (HRS) system. The HRS transponder is used as a sensing node placed on the surface of the target’s thoracic cavity. Through an I/Q-modulated harmonic reader architecture, information from the HRS is acquired. The amplitude and phase information demodulated by the HRS transceiver are used for vital sign detection and target localization, respectively. In this study, we successfully conduct the wireless harmonic sensing experiment for vital sign sensing with the capability for transponder localization. The measured vital sign signals show a good consistency with commercial electrocardiograph (ECG) reference signals in terms of heartbeat intervals. Furthermore, the system achieves precise localization of the transponder, with a positioning error of less than 6 cm with the probability of 95%. Empirical evidence demonstrates that the proposed HRS-based system exhibits superior and more stable detection performance compared with conventional passive harmonic methods. Furthermore, our proposed active HRS extends the maximum detection range of passive harmonic tags from 2 to 3 m. This improvement arises from the system’s active architecture, which integrates a reflective amplifier to enhance signal strength by 12 dB at 1.8 GHz. The experimental results also indicate that the wireless sensing architecture based on HRS in this work is a potential candidate for future healthcare systems.

Many existing signal-to-noise ratio (SNR) estimators were designed and evaluated for conventional one-hop communications systems. However, for a relaying system, it is the end-to-end SNR that determines the system performance. In this paper, we will fill this gap by evaluating the performances of the existing SNR estimators in a dual-hop relaying system used for each hop. The probability density functions of the SNR estimators are first derived, whose parameters are fitted as functions of the sample size and the true value of SNR. Using them, the cumulative distribution functions of the end-to-end SNR and the bit error rate performance for a relaying system are derived. Numerical results show that the squared signal-to-noise variance estimator has the best performance for small SNRs and the second-order fourth-order moments estimator has the best performance for large SNRs, while the signal-to-variation ratio estimator has the worst performance, among the existing SNR estimators, for AF relaying systems.

In general multiple medical devices orthogonal frequency-division multiplexing (OFDM) communication systems, all the interfering medical users are legitimate but will cause disturbance to the desired user. In this work, we evaluate three deep learning (DL) algorithms: fully connected deep neural networks, convolutional neural networks, and long short-term memory neural networks for signal processing and detection in uncoded multiple medical devices OFDM communications systems. The bit error rates (BER) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio, signal-to-noise ratio, the number of interferences, and modulation type are investigated. Numerical results show that DL methods outperform LMMSE under different multiple medical device interference situations and are robust when the wireless channel has high variability. Also, DL methods are proven to have strong anti-interference ability and are useful in multiple medical devices OFDM systems.

Three novel structures using simultaneous wireless information and power transfer in energy-harvesting (EH) amplify-and-forward relaying are investigated in this paper. Different combinations of time-switching and power-splitting (PS) EH protocols are studied. Three dynamic schemes are proposed as channel estimation PS (CEPS), data transmission PS (DTPS), and combination PS (CPS). From source to relay in these schemes, the data packet includes three parts: channel estimation, data transmission, and EH. From relay to destination in these schemes, the data packet includes two parts: data transmission and channel estimation. Closed-form expressions for the cumulative distribution function of the end-to-end signal-to-noise ratio for the three structures are derived. Using these expressions, achievable rate and bit-error-rate are derived. Different parameters are examined. Numerical results show the optimal splitting ratio for the channel estimation, EH, and data transmission, when the packet size is fixed.