Internet of Things (IoT) is able to provide various physical objects to exchange their information through the 6G wireless communication network. However, with the large increasing number of the IoT devices (IoDs), the deployment of IoDs faces two basic challenges, i.e., spectrum scarcity and energy limitation. Cooperative spectrum sharing and simultaneous wireless information and power transfer (SWIPT) provide effective ways to improve the spectrum and energy efficiency. In this article, two SWIPT cooperative spectrum sharing methods are proposed to improve the energy and spectrum efficiency for 6G-enabled cognitive IoT network, in which IoDs access to the primary spectrum by serving as orthogonal frequency-division multiplexing (OFDM) relay with the energy harvested from the received radio-frequency (RF) signal. Specifically, in phase1, the IoDs transmitter (DT) in the cognitive IoT network performs information decoding and energy harvesting with the received RF signal. In phase2, DT transmits the signals of the primary system and itself to the corresponding receiver by utilizing orthogonal subcarriers with the harvested energy to avoid the interference. Achievable rates of the cognitive IoT system with amplify-and-forward (AF) and decode-and-forward (DF) relaying mode are maximized through joint power and subcarrier optimization, while ensuring the target rate of the primary system. Simulation results are performed to illustrate the improvement of the spectrum and energy efficiency.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this paper, an architecture design of smart agriculture is firstly proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers' pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

Due to the single and limited prior information in electromagnetic tomography (EMT), the existing reconstruction methods are often unable to reconstruct the image with high accuracy for different kinds of flow patterns. In this article, an image reconstruction algorithm based on the sequential Monte Carlo principle for EMT is proposed. It can increase the diversity of samples to extract more image features for EMT images reconstruction and maintain a high image reconstruction effect for different flow patterns. First, the EMT image reconstruction process is modeled as a dynamic time-varying system to search for the optimal estimation in state space. At the same time, controllable adjustment of probability density distribution can improve the reconstruction effect under different flow patterns. Second, samples are obtained from the initial sample space, and their weights are updated according to the samples’ information. The numbers of effective samples and the diversity of samples are increased by resampling. Finally, the system state estimation is calculated according to the samples and their updated weights, and the system state is updated iteratively until the optimal solution is obtained. Simulation results have shown that the proposed method is superior to other existing methods in image error and correlation coefficient, and has better imaging quality.

The recent finite rate of innovation (FRI) framework provides effective sub-Nyquist sampling of pulse streams, allowing recovery of such signals from a set of Fourier coefficients. In this brief, a multi-channel FRI sampling system is presented to sample distinct bands of Fourier coefficients. This is achieved through modulating the desired spectrum band to baseband and then filtering with a low-pass filter. However, the modulation process will lead to the spectrum aliasing and unavailability. A modulation frequency selection strategy is proposed to solve this problem, which allows obtaining reconfigurable Fourier coefficients from the aliasing spectrum. Combining with multi-channel structure, we present a simple and efficient way to sample distinct bands of the pulse streams' spectrum. Finally, a design and implementation of the hardware prototype is presented. Simulation and hardware experiment results demonstrate the effectiveness and robustness of our system.

To prevent lifestyle diseases, portable heartbeat detection systems by electrocardiograms (ECGs) for daily life monitoring have attracted attentions. For portable systems, power consumption is critical and it is thus beneficial to reduce the sampling frequency at the point of acquisition. Recent developed variable pulsewidth finite rate of innovation (VPW-FRI) framework provides an effective way for low-rate sampling and compression of ECG signals. Unfortunately, noise and model mismatch will induce inaccuracies to such scheme in the reconstruction process. In this article, a sub-Nyquist sampling scheme based on the extension of VPW model is proposed for ECG signals, which reduce the model dismatch error, and improve the reconstruction quality under model mismatch and noise scenario. In the proposed scheme, ECG signal is modeled as the differentiated VPW model to reduce the model mismatch error. Then a FRI sampling framework based on the differentiated VPW model is proposed to sample the ECG signal at a sub-Nyquist rate. After the ECG signal is sampled, some consecutive Fourier series coefficients are calculated according to the samples. Then a modified annihilating filter algorithm is proposed to estimate parameters of the ECG signal and reconstruct the ECG signal. Finally, simulation experiments with real ECG signals from Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia Database have shown that the proposed scheme outperformed the traditional methods for model mismatch. The signal-to-residual ratio (SRR) of the reconstructed signal of the proposed scheme is higher than that of the VPW-FRI scheme.