Device-free respiratory monitoring has attracted significant attention due to its potential applications in sleep disorders, psychopathology, and cardiology. It enables respiratory monitoring in a device-free and contact-free manner by analyzing the influence pattern of human respiratory on surrounding wireless signals, such as mmWave signals. Although remarkable progress has been achieved in this task when the targets remain stationary, the respiratory monitoring will fail when the target moves freely. In this paper, we propose a device-free real-time respiratory abnormality monitoring method based on mmWave signal segmentation to solve the aforementioned problem. Specifically, we design the physical state assessment strategy to obtain the real-time states of the target, including positional movement, large-scale activities in place, and micro motions in place. We propose the Doppler signal segmentation method to extract the micro motions signals when the target position remains unchanged. We present the multi-frame joint analysis method to obtain the frequency of micro motions based on the extracted micro motion signals, thereby eliminating interference and achieving real-time respiratory abnormality monitoring. To validate the effectiveness of the proposed methods, we conduct extensive experiments on a 77GHz mmWave testbed. The results indicate that the proposed method is feasible for achieving real-time respiratory abnormality monitoring even when the target moves freely.

This paper aims to enhance user fairness in unmanned aerial vehicles (UAVs) enabled cell-free wireless networks. We consider a scenario where multiple UAVs serve as access points for ground users (UEs). We jointly design the UAV deployment, power allocation, UAV-UE association, and pilot assignment to maximize the minimum downlink user rate, while taking account the impact of pilot contamination. The design is formulated as a mixed-integer non-convex optimization problem. To circumvent the problem non-convexity and facilitate the design of a computationally efficient suboptimal solution, a series of transformations and approximations are proposed based on the particle swarm optimization and successive convex approximation techniques. Simulation results are presented to demonstrate the effectiveness and advantages brought by the joint design for enhancing user fairness. Furthermore, our study provides new insights into the feasibility of using sparse association to reduce costs while maintaining performance, especially in scenarios with a large number of users.

This paper investigates space-aerial assisted mixed cloud-edge computing services for space-aerial integrated networks, where unmanned aerial vehicles (UAVs) provide edge computing services and one satellite (SAT) provides ubiquitous cloud computing services. To effectively and efficiently schedule such services under constraints on the available resources of computational capacity, energy, and communications of UAVs and the SAT, a problem for minimizing the total computing and offloading delay is formulated. A learning algorithm for handling the reformulated problem is proposed that alternatively performs convex optimization based computation capacity allocation (involving real variables) and double deep Q-learning (DDQL) based task assignment (involving binary variables) among all UAVs and the SAT. Extensive simulation results are presented to demonstrate that the efficacy of the proposed algorithm is significantly superior over some state-of-the-art reinforcement learning-based methods in terms of the algorithm running time and system scalability in the training stage and total computing and offloading delay in the testing stage.

This article studies the coverage performance of unmanned aerial vehicle (UAV)-assisted simultaneous wireless information and power transfer (SWIPT) networks under the nonlinear and linear energy harvesting (EH) models in the rich scattering scenarios, including smart farming and smart ranching, where the None-Line-of-Sight (NLoS) links are the dominate component of the wireless channel. Multiple UAVs are equipped with directional antennas to transfer information and energy to ground users (GUs). Power splitting (PS) or time switching (TS) architecture is employed at GUs. In order to evaluate the system performance in fading channels, the information and energy coverage probabilities of the system are discussed, and by using the stochastic geometry approach and the approximate scaling method, the general and lower bound explicit expressions of the coverage probabilities are derived. Numerical results show that the coverage performance of PS-based systems is superior to that of TS-based systems. Moreover, although the linear EH model yields better results than the nonlinear one, as the linear EH model is too ideal, its yielded results may mismatch practical EH circuits, and the ones yielded by the nonlinear EH model is much closer to practice, as the nonlinear EH model is based on real data measurement. Additionally, the nonlinear EH model has a relatively small effect on the harvested energy coverage probability, and the linear EH model introduces greater bias for the TS-based system than that for PS-based one.

Unmanned aerial vehicles (UAVs) with huge-capacity batteries could be employed to wirelessly charge the ground sensor users (GSUs) and enhance the coverage of aerial wireless networks in outdoor Internet of Things (IoT). This paper investigates the information and energy coverage of UAV-enabled simultaneous wireless information and power transfer (SWIPT) networks. Both power splitting (PS) and time switching (TS) receiver architectures are considered. By using stochastic geometry approach, the general and explicit expressions of the information coverage probability (ICP), the energy coverage probability (ECP) and the joint information and energy coverage probability (JIECP) are derived under the nonlinear and linear energy harvesting (EH) models, respectively. Particularly, the Laplace transform and the probability generating functional (PGFL) are used to derive the ICP. And, Campbell’s theorem and the maximum function are applied to obtain the ECP and the JIECP, respectively. To achieve the optimal UAVs’ deployment density, the maximization optimization problems are formulated for the PS-based and TS-based systems, respectively. By using the series expansion of$Q(x)$($Q$-function) with large$x$, the closed-form approximating optimal solutions to the formulated problems are obtained. Monte Carlo simulations validate the correction of our obtained theoretical results, and numerical results show that the performance of the PS-based system is superior to that of the TS-based one. Moreover, when the energy requirement of GSUs or the transmit power of UAVs is relatively large, or when the information requirement of GSUs or the UAV deployment density is relatively small, compared with the nonlinear EH model, the analysis bias caused by traditional linear EH model is relatively large and in these cases, traditional linear EH model cannot be used to replace the nonlinear EH one for the system performance analysis or optimal system design.

Wireless Internet-of-Things (IoT) networks empowered by blockchain have became a promising architecture to establish trust and consensus mechanisms in a distributed manner. However, the computational complexity and limited on-board energy of wireless devices impose great challenges on applying blockchain into IoT networks. To address this issue, this work introduces multiple mobile edge computing (MEC) to provide sufficient computational power for miners (i.e., IoT devices). As such, the computation-intensive tasks of the miners can be either computed locally or offloaded to some certain MEC servers to fully exploit the computation resources. Moreover, to decrease the energy consumption of the IoT networks, an optimization problem is formulated to maximize the energy efficiency of IoT devices by jointly optimizing the computation mode selection and power allocation. Since the formulated problem is generally intractable with mixed-integer variables, an Fmincon-based algorithm is proposed, which guarantees a globally optimal solution. To further reduce the computational complexity of the proposed optimal method, a Deep Neural Network (DNN)-based deep learning method is applied to facilitate the computation of the proposed algorithm. Finally, numerical results demonstrate the advantages of the proposed network architecture and the algorithm in terms of both the energy and computational efficiency.

This paper investigates the information-energy (I-E) region for simultaneous wireless information and power transfer (SWIPT) system in mobility scenarios, where a moving transmitter transmits information and energy to a power splitting (PS)-based receiver. An optimization problem is formulated to explore the system I-E region under the nonlinear energy harvesting (EH) model by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver. Since the problem is nonconvex, a successive convex approximate-based (SCA-based) algorithm is proposed, which is able to find the sub-optimal solution with low complexity. For comparison, the I-E region of the system under the linear model is also studied, where some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the linear EH model, the nonlinear EH model yields a smaller I-E region due to the limitations of EH circuit features. Nevertheless, using the nonlinear EH model avoids the false achievable I-E region for practical mobile SWIPT systems. Besides, it shows that the higher moving speed yields the smaller I-E region. Moreover, with the increment of the required information amount, the harvested energy bias caused by the linear EH model decreases, but the bias ratio increases.

This paper investigates the simultaneous wireless information and power transfer (SWIPT) networks with coexisting power-splitting users (PSUs) and time-switching users (TSUs) under the nonlinear energy harvesting (EH) model, where a multiantenna hybrid access point (H-AP) transmits information and power to multiple PSUs and TSUs. For such a network, an optimization problem is formulated to minimize the required transmit power at the H-AP subject to users' information rate and harvested energy constrains by jointly optimizing the H-AP's transmit beamforming vectors, PSUs' power splitting (PS) ratios, and TSUs' time switching (TS) factors. Due to the interferences among PSUs and TSUs, and the nonlinear EH model, the problem is nonconvex and has no known solution method. Thus, a two-layer algorithm is first presented based on semidefinite relaxation (SDR) and it is theoretically proved that the global optimum is achieved. However, since 1-D search is adopted by the two-layer algorithm, which may be too computationally exhaustive, a successive convex approximate-based (SCA-based) algorithm is then proposed as an alternative, which is able to find the near-optimal solution with low complexity by using the first-order approximation. The numerical results show that with the same EH requirements, TSUs are more likely to enter into the saturation region compared with PSUs, but their EH efficiency is higher than that of PSUs. It is also shown that the minimal required transmit power under the nonlinear EH model is much lower than that under the linear one. Although after the saturation point of the nonlinear EH model, the linear one yields a lower required transmit power, it is a fake result, because the linear EH model mismatches the nonlinearity of practical EH circuits.

This paper investigates the joint optimal beamforming and power-splitting receiver architecture design for simultaneous information and power transfer (SWIPT) under non-linear energy harvesting (EH) circuit environment, where hybrid access point (H-AP) equipped with multiple antennas simultaneously transmits information and power to multiple single-antenna users. For such a system, in order to achieve green communication design, we formulate an optimization problem to minimize the total transmit power of H-AP subjecting to the required signal-to-interference-plus- noise ratio (SINR) and the harvested power constrains at each user under non-linear EH model. Since the problem is non-convex, the relaxed semidefinite program (SDP) is used to solve it, and it is proved that the semidefinite relaxation (SDR) process is tight and our optimal solution achieves the global optimum. Numerical results show that a considerable gain could be achieved if the SWIPT beamforming vector and the power splitting ratios are jointly designed under the non-linear EH model compared with traditional linear EH model, since the non-linear EH model captures and matches the real EH circuits' non-linear features. Moreover, the feasible region of traditional linear EH model in the non-linear EH circuit environment is also characterized.