Search

Abstract Background Aging negatively impacts tissue repair, particularly in skeletal muscle, where the regenerative capacity of muscle stem cells (MuSCs) diminishes with age. Although aerobic exercise is known to attenuate skeletal muscle atrophy, its specific impact on the regenerative and repair capacity of MuSCs remains unclear. Methods Mice underwent moderate‐intensity continuous training (MICT) from 9 months (aged + Ex‐9M) or 20 months (aged + Ex‐20M) to 25 months, with age‐matched (aged) and adult controls. Histological examinations and MuSC transplantation assays assessed aerobic exercise effects on MuSC function and muscle regeneration. CCN2/connective tissue growth factor modulation (overexpression and knockdown) in MuSCs and AICAR supplementation effects were explored. Results Aged mice displayed significantly reduced running duration (65.33 ± 4.32 vs. 161.9 ± 1.29 min, mean ± SD, P

Objective To summarize the clinical characteristics of peripheral nerve hyperexcitability syndrome (PNHS). Methods and Results Ten patients with PNHS were admitted to He'nan Provincial People's Hospital from October 2018 to April 2022, with the clinical phenotypes of Morvan syndrome in 5 cases, Issacs syndrome in 4 cases, and cramp-fasciculation syndrome (CFS) in one case. There were 10 cases with clinical manifestations of muscle twitching, 8 of dysautonomia, 7 of central nervous system symptoms, 7 of limb weakness, 6 of sensory deficits, and 2 of cramping and muscle stiffness. Five cases were positive for autoimmune antibodies. Two cases had MRI abnormalities located in the brainstem, left occipital lobe and right thalamus. Ten cases had EMG abnormalities, including afterdischarges of F - wave in 10 cases, unit potential of spontaneous motion abnormalities in 10 cases, fasciculation potentials in 10 cases, myokymic discharges in 8 cases, and neuromyotonic and myokymic discharges in 3 cases. Three cases underwent only symptomatic therapy, and 7 cases underwent symptomatic therapy and immunomodulatory treatments. Except for one case who lost of follow-up, 5 cases had a good prognosis, 2 cases with poor prognosis, and 2 cases died. Conclusions PNHS is a heterogeneous group of clinical manifestations. Electrophysiological appearance of afterdischarges may be a sensitive indicator for the diagnosis of PNHS.

Temperature swing adsorption is one of most important means of the enrichment of xenon, and temperature is an important factor affecting adsorbent performance for xenon. In this paper, the dynamic adsorption experimental platform at ultra-low temperatures was designed, based on which the adsorption characteristics of carbon molecular sieve for xenon was studied within the temperature range of -110 ℃ to -60 ℃. The effects of xenon volume fraction, gas flow rate, inlet pressure, and inner diameter of the adsorption column on xenon adsorption performance were analyzed at -110 ℃. The results show that at -110 ℃, the volume fraction of xenon and flow rate have the most significant effects on the dynamic adsorption coefficient of xenon, while the effects of the inlet pressure and the inner diameter of the adsorption column are less than the above two factors within a certain range.

We evaluated the expression profiles of differentially expressed miRNAs (DEmiRNAs) involved in human fetal skin development via high-throughput sequencing to explore the expression difference and the regulatory role of miRNA in different stages of fetal skin development. Analysis of expression profiles of miRNAs involved collecting embryo samples via high-throughput sequencing, then bioinformatics analyses were performed to validate DEmiRNAs. A total of 363 miRNAs were differentially expressed during the early and mid-pregnancy of development, and upregulated DEmiRNAs were mainly concentrated in the let-7 family. The transfection of let-7b-5p slowed down HaCaT cell proliferation and promoted apoptosis, as evidenced by the cell counting kit-8 assay, quantitative real-time polymerase chain reaction, and flow cytometry. The double luciferin reporter assay also confirmed let-7b-5p and ΔNp63 downregulation through the combination with the 3ʹ-untranslated region of ΔNp63. Moreover, treatment with a let-7b-5p inhibitor upregulated ΔNp63 and activated the phosphoinositide 3-kinase (PI3K)-protein kinase B (AKT) signaling pathway. The let-7b-5p caused a converse effect on HaCaT cells because of Np63 upregulation. Let-7b-5p regulates skin development by targeting ΔNp63 via PI3K-AKT signaling, contributing to future studies on skin development and clinical scar-free healing.

Abstract As massive MIMO is a key technology in the future sixth generation (6G), the large-scale antenna arrays are widely considered in direction-of-arrival (DOA) estimation for they can provide larger aperture and higher estimation resolution. However, the conventional fully digital architecture requires one radio-frequency (RF) chain per antenna, and this is challenging for the high hardware costs and much more power consumption caused by the large number of RF chains. Therefore, an overlapped subarray (OSA) architecture-based hybrid massive MIMO array is proposed to reduce the hardware costs, and it can also have better DOA estimation accuracy compared to non-overlapped subarray (NOSA) architecture. The simulation results also show that the accuracy of the proposed OSA architecture has $$6^{\circ }$$ 6 ∘ advantage over the NOSA architecture with signal-to-noise ratio (SNR) at 10 dB. In addition, to improve the DOA estimation resolution, a deep learning (DL)-based estimator is proposed by combining convolution denoise autoencoder (CDAE) and deep neural network (DNN), where CDAE can remove the approximation error of sample covariance matrix (SCM) and DNN is used to perform high-resolution DOA estimation. From the simulation results, CDAE-DNN can achieve the accuracy lower bound at $$\textrm{SNR}=-8$$ SNR = - 8 dB and the number of snapshots $$N=100$$ N = 100 , this means it has better performance in poor communication situation and can save more software resources compared to conventional estimators.

Territorial space ecological restoration has become a significant strategy for the construction of China's ecological civilization. It is also an important strategic task related to national ecological security and human well-being. The continuous expansion of space in urban agglomerations and the high-intensity development of cities have exacerbated the fragmentation of natural landscapes. The ecological security of urban agglomerations and the ecological restoration of territorial spaces have become key issues of common concern to the government and scholars. Currently, the research on ecological restoration of territorial space mostly focuses on the identification of ecological restoration areas based on the key components of ecological security patterns and rarely discusses the coordinated protection and restoration of urban agglomerations by combining the ecological security patterns with human activities. It is particularly necessary to identify the ecological restoration areas and build a technical paradigm for ecological protection and territorial space restoration that can provide a basic support for the ecological integration, construction, and sustainable development of urban agglomerations. Based on ecological network construction and human disturbance assessment, this study diagnoses the territorial space ecological restoration areas of Guangdong-Hong Kong-Macao Greater Bay Area and proposes ecological protection and restoration strategies for the territorial space ecological source restoration area, ecological corridor restoration area, and stepping stone restoration area. The results showed that: (1) Human activities in the Guangdong-Hong Kong-Macao Greater Bay Area had high interference intensities and a wide impact on the ecological environment; areas with high and relatively high interference accounted for 48.65% of the total area, mainly concentrated in the central part of the Greater Bay Area. (2) The ecological network of Guangdong-Hong Kong-Macao Greater Bay Area presented a relatively stable characteristic pattern of "2 breadth lines and 4 length lines". The 2-breadth lines included the continuous mountains in the north and the ecological protection belt of the southern coastal waters. The 4-length lines included the eastern and western land corridors and the central water corridors. (3) The ecological source restoration area of the Guangdong-Hong Kong-Macao Greater Bay Area was 4,687.42 km2, the ecological corridor restoration length was 1,362.71 km, and the stepping stone restoration area was 833.82 km2, mainly distributed in Zhaoqing, Huizhou, and Jiangmen. To limit human disturbance and restore ecological functions, strategies for territorial space ecological restoration were proposed based on the ecological network function and structural integrity; (4) It is urgent for government departments to overcome the concept of regionalism and build a consensus on the co-disposal of ecological restoration in the territorial space. The construction of cross-domain collaborative governance institutions will efficiently promote the ecological restoration of cross-regional territorial spaces. Considering both human and ecological systems, this study discussed the ecological restoration schema in key areas of ecological networks, coastal ecological restoration areas, and trans-regional ecological restoration areas. The method for urban agglomeration territorial space ecological restoration was formed, including "ecological network pattern-human activity disturbance-restoration area diagnosis-restoration strategy," which will provide a framework for promoting the territorial space ecological protection and restoration in the Guangdong-Hong Kong-Macao Greater Bay Area.

In this paper, the dominant factor affecting the performance of active intelligent reflecting surface (IRS) aided wireless communication networks in Rayleigh fading channel, namely the average signal-to-noise ratio (SNR) $\gamma _{0}$ at IRS, is studied. Making use of the weak law of large numbers, the simple asymptotic expressions for the received SNR at user and the average SNR at IRS are derived as the number $N$ of IRS elements goes to medium-scale and large-scale. When $N$ tends to large-scale, the asymptotic received SNR at user is proved to be a linear increasing function of a product of $\gamma _{0}$ and $N$ . Subsequently, when the base station (BS) transmit power is fixed, there exists an optimal limited reflective power at IRS. At this point, more IRS reflect power will degrade the SNR performance. Additionally, under the total power sum constraint of the BS transmit power and the power reflected by the IRS, an optimal power allocation (PA) strategy is derived and shown to achieve 0.83 bit rate gain over equal PA. Finally, an IRS with finite phase shifters being taken into account, generates phase quantization errors, and further leads to a degradation of receive performance. The corresponding closed-form performance loss expressions for user’s asymptotic SNR, achievable rate (AR), and bit error rate (BER) are derived for active IRS. Numerical simulation results show that a 3-bit discrete phase shifter is required to achieve a trivial performance loss for a large-scale active IRS.

Due to the power consumption and high circuit cost in antenna arrays, the practical application of massive multiple-input multiple-output (MIMO) in the sixth generation (6G) and future wireless networks is still challenging. Employing low-resolution analog-to-digital converters (ADCs) and hybrid analog and digital (HAD) structure is two low-cost choices with acceptable performance loss. In this paper, the combination of the mixed-ADC architecture and HAD structure employed at receiver is proposed for direction of arrival (DOA) estimation, which will be applied to the beamforming tracking and alignment in 6G. By adopting the additive quantization noise model, the exact closed-form expression of the Cramér-Rao lower bound (CRLB) for the HAD architecture with mixed-ADCs is derived. Moreover, the closed-form expression of the performance loss factor is derived as a benchmark. In addition, to take power consumption into account, energy efficiency is also investigated in our paper. The numerical results reveal that the HAD structure with mixed-ADCs can significantly reduce the power consumption and hardware cost. Furthermore, that architecture is able to achieve a better trade-off between the performance loss and the power consumption. Finally, adopting 2–4 bits of resolution may be a good choice in practical massive MIMO systems.

Obstructive sleep apnea (OSA), one of the most common sleep-related breathing disorders, contributes as a potentially life-threatening disease. In this paper, a wearable functional near-infrared spectroscopy (fNIRS) system for OSA monitoring is proposed. As a non-invasive system that can monitor oxygenation and cerebral hemodynamics, the proposed system is dedicated to mapping the pathogenic characteristics of OSA to dynamic changes in blood oxygen concentration and to constructing an automatic approach for assessing OSA. An algorithm including feature extraction, feature selection, and classification is proposed to signals. Permutation entropy(PE), for quantitative measuring the complexity of time series, is firstly involved to characterize the features of the physiological signals. Subsequently, the principal component analysis (PCA) for feature dimensionality reduction and support vector machine (SVM) algorithm for OSA classification are applied. The proposed method has been validated on a dataset that collected by the wearable system. It includes 40 subjects and composes of normal, and various severity cessation of breathing (e.g., mild, moderate, and severe). Experimental results exhibit that the proposed system can effectively distinguish OSA and non-OSA subjects, with an accuracy of 91.89%. The proposed system is expected to pave the novel perspective for OSA assessment in terms of cerebral hemodynamics.

Sleep staging is the essential step in sleep quality assessment and sleep disorders diagnosis. However, most current automatic sleep staging approaches use recurrent neural networks (RNN), resulting in a relatively large training burden. Moreover, these methods only extract information of the whole epoch or adjacent epochs, ignoring the local signal variations within epoch. To address these issues, a novel deep learning architecture named segmented attention network (SAN) is proposed in this paper. The architecture can be divided into feature extraction (FE) and time sequence encoder (TSE). The FE module consists of multiple multiscale CNN (MMCNN) and residual squeeze and excitation block (SE block). The former extracts features from multiple equal-length EEG segments and the latter reinforced the features. The TSE module based on a multi-head attention mechanism could capture the temporal information in the features extracted by FE module. Noteworthy, in SAN, we replaced the RNN module with a TSE module for temporal learning and made the network faster. The evaluation of the model was performed on two widely used public datasets, Montreal Archive of Sleep Studies (MASS) and Sleep-EDFX, and one clinical dataset from Huashan Hospital of Fudan University, Shanghai, China (HSFU). The proposed model achieved the accuracy of 85.5%, 86.4%, 82.5% on Sleep-EDFX, MASS and HSFU, respectively. The experimental results exhibited favorable performance and consistent improvements of SAN on different datasets in comparison with the state-of-the-art studies. It also proved the necessity of sleep staging by integrating the local characteristics within epochs and adjacent informative features among epochs.

Abstract Autophagy is a conserved lysosomal degradation pathway where cellular components are dynamically degraded and re-processed to maintain physical homeostasis. However, the physiological effect of autophagy appears to be multifaced. On the one hand, autophagy functions as a cytoprotective mechanism, protecting against multiple diseases, especially tumor, cardiovascular disorders, and neurodegenerative and infectious disease. Conversely, autophagy may also play a detrimental role via pro-survival effects on cancer cells or cell-killing effects on normal body cells. During disorder onset and progression, the expression levels of autophagy-related regulators and proteins encoded by autophagy-related genes (ATGs) are abnormally regulated, giving rise to imbalanced autophagy flux. However, the detailed mechanisms and molecular events of this process are quite complex. Epigenetic, including DNA methylation, histone modifications and miRNAs, and post-translational modifications, including ubiquitination, phosphorylation and acetylation, precisely manipulate gene expression and protein function, and are strongly correlated with the occurrence and development of multiple diseases. There is substantial evidence that autophagy-relevant regulators and machineries are subjected to epigenetic and post-translational modulation, resulting in alterations in autophagy levels, which subsequently induces disease or affects the therapeutic effectiveness to agents. In this review, we focus on the regulatory mechanisms mediated by epigenetic and post-translational modifications in disease-related autophagy to unveil potential therapeutic targets. In addition, the effect of autophagy on the therapeutic effectiveness of epigenetic drugs or drugs targeting post-translational modification have also been discussed, providing insights into the combination with autophagy activators or inhibitors in the treatment of clinical diseases.

Passive geolocation by multiple unmanned aerial vehicles (UAVs) covers a wide range of military and civilian applications including rescue, wild life tracking and electronic warfare. The sensor-target geometry is known to significantly affect the localization precision. The existing optimal sensor placement strategies mainly work on the cases without any constraints on the sensor locations. However, UAVs cannot fly/hover simply in arbitrary region due to realistic constraints, such as the geographical limitations, the security issues, and the max flying speed. In this paper, optimal geometrical configurations of UAVs in received signal strength (RSS)-based localization under region constraints are investigated. Employing the D-optimal criteria, i.e., maximizing the determinant of Fisher information matrix (FIM), such optimal problem is formulated. Based on the rigorous algebra and geometrical derivations, optimal and also closed-form configurations of UAVs under different flying states are proposed. Finally, the effectiveness and practicality of the proposed configurations are demonstrated by simulation examples.

Since the 21st century, the term "scene" has been widely used in various fields, mainly involving sociology, communication, education, the Internet, commercial marketing and so on. Academic circles have also carried out scene-related research to form their own definitions and theories. With the continuous innovation of technical means, the connotation of a scene is constantly enriched, from the face-to-face physical space dimension to the multi-dimensional information space, and gradually evolved into the scene characteristics of spatio-temporal integration. However, there is still a lack of complete theoretical research and practical discussion on what is the scene, what are the elements of the scene, and how to combine the specific scene to guide and carry out the scene planning and design in different fields. The existing scene theory cannot be well applied in the field of urban development, and cannot help the scene related research of coordinated development of urban agglomeration. By combing the development context of scene theory at home and abroad, this study makes an in-depth interpretation and analysis of the connotation and characteristics of the scene. It is believed that the scene contains six characteristic elements, such as time, space, object, problem, path and value. The scene is a kind of specific interactive relationship formed by a series of actions, based on the key issues and objectives of the events under specific spatio-temporal conditions, with technology and resources as the media. The construction of the scene can meet the needs of stakeholders and create and realize value. The theoretical system of scene planning including scene elements, scene hierarchy characteristics and scene planning methods is constructed. It is considered that scene planning is a systematic process to realize specific social, economic or ecological functions and values by integrating existing resources and technologies to carry out scene construction and design, focusing on key issues and objectives with stakeholders as its center, under the environmental factors of social and economic development, government policies and technological innovation. From the perspectives of scene recognition, scene construction, scene evaluation and scene application, the new exploration of scene planning tools is realized to guide scene practice in different fields. Taking the coordinated development of urban agglomeration in the Guangdong-Hong Kong-Macao Greater Bay Area as an example, this study excavates the current problems and development objectives of the Greater Bay Area based on the scene planning method, identifies 100 important scene units representing the coordinated development direction of urban agglomeration in the Greater Bay Area, and identifies and analyzes each scene element for these scene units, so as to provide basic support for further scene construction. Finally, taking the ecological corridor construction of urban agglomeration in the Guangdong-Hong Kong-Macao Greater Bay Area as an application example, the scene planning method is preliminarily applied to analyze the scene elements, so as to provide a basis for the next practical exploration work such as scene evaluation and scene application demonstration. The theoretical system of scene planning is not only a further extension of the scene concept, but also an overall coordination tool to promote the coordinated development of urban agglomeration. It can provide theoretical basis and practical guidance for the future scene research on the coordinated development of urban agglomeration, and help the collaborative construction of urban agglomeration. In the future, it is suggested to carry out specific scene analysis and scene design on the industry, population, transportation, environment, infrastructure and public services of the GBA urban agglomeration, and gradually realize the in-depth exploration of the coordinated development theory and mechanism innovation of the Guangdong-Hong Kong-Macao Greater Bay Area through the scene planning theory.

Intelligent reflecting surface (IRS) is a revolutionary and low-cost technology for boosting the spectrum and energy efficiencies in future wireless communication network. In order to create controllable multipath transmission in the conventional line-of-sight (LOS) wireless communication environment, an IRS-aided directional modulation (DM) network is considered. In this paper, to improve the transmission security of the system and maximize the receive power sum (Max-RPS), two alternately optimizing schemes of jointly designing receive beamforming (RBF) vectors and IRS phase shift matrix (PSM) are proposed: Max-RPS using general alternating optimization (Max-RPS-GAO) algorithm and Max-RPS using zero-forcing (Max-RPS-ZF) algorithm. Simulation results show that, compared with the no-IRSassisted scheme and no-PSM optimization scheme, the proposed IRS-assisted Max-RPS-GAO method and Max-RPS-ZF method can significantly improve the secrecy rate (SR) performance of the DM system. Moreover, compared with the Max-RPS-GAO method, the proposed Max-RPS-ZF method has a faster convergence speed and a certain lower computational complexity.

In this paper, with the help of an intelligent reflecting surface (IRS), the source (S) and destination (D) exchange information through the two-way decode-and-forward relay (TW-DFR). We focus on the phase optimization of IRS to improve the system sum rate performance. Firstly, a maximizing receive power sum (Max-RPS) method is proposed via eigenvalue decomposition (EVD) with an appreciable system sum rate enhancement, which is called Max-RPS-EVD. To further achieve a higher system sum rate, a method of maximizing minimum rate (Max-Min-R) is proposed with high complexity. To reduce its complexity, a low-complexity method of maximizing the sum rate (Max-SR) via general power iterative (GPI) is proposed, which is called Max-SR-GPI. Simulation results show that the proposed three methods outperform the random phase method, especially the proposed Max-SR-GPI method is the best one achieving at least 20% system sum rate gain over random phase. Additionally, it is also proved the optimal system sum rate can be achieved when TW-DFR and IRS are located in the middle of S and D.

Abstract Iterative learning control (ILC) has been widely used to improve motion performance when reference trajectories and external disturbances are strictly repetitive. However, the occurrence of non‐repetitive trajectories and disturbances would significantly deteriorate the performance of traditional ILC methods. To solve this problem, a time‐iteration‐domain integrated learning control (TIDLC) scheme is proposed for enhancing robustness against non‐repetitive trajectories and disturbances. The TIDLC scheme consists of an ILC term and a time‐domain compensator (TC) term. While the ILC term that performs learning in the iteration‐domain preserves excellent performance under repetitive tasks, the TC term that is updated in the time‐domain can compensate for the variations of trajectories and disturbances. Stability and performance analyses are discussed in both the time‐domain and iteration‐domain. Experimental results on a permanent magnet linear synchronous motor (PMLSM) positioning system verify the validity of the proposed scheme.

As excellent tools for aiding communication, an intelligent reflecting surface (IRS) and an unmanned aerial vehicle (UAV) can extend the coverage area, remove the blind area, and achieve a dramatic rate improvement. In this paper, we improve the secrecy rate (SR) performance of directional modulation (DM) networks using an IRS and UAV in combination. To fully explore the benefits of the IRS and UAV, two efficient methods are proposed to enhance the SR performance. The first approach computes the confidential message (CM) beamforming vector by maximizing the SR, and the signal-to-leakage-noise ratio (SLNR) method is used to optimize the IRS phase shift matrix (PSM), which is called Max-SR-SLNR. To reduce the computational complexity, the CM, artificial noise (AN) beamforming, and IRS phase shift design are independently designed in the following method. The CM beamforming vector is constructed based on the maximum ratio transmission (MRT) criteria along the channel from Alice-to-IRS, the AN beamforming vector is designed by null-space projection (NSP) on the remaining two channels, and the PSM of the IRS is directly given by the phase alignment (PA) method. This method is called the MRT-NSP-PA. The simulation results show that the SR performance of the Max-SR-SLNR method outperforms the MRT-NSP-PA method in the cases of small-scale and medium-scale IRSs, and the latter approaches the former in performance as the IRS tends to a larger scale.

Driven by the rapidly growing demand for information security, covert wireless communication has become an essential technology and attracted tremendous attention. However, traditional wireless covert communication is continuously exposing the inherent limitations, creating challenges around deployment in environments with a large number of obstacles, such as cities with high-rise buildings. In this paper, we propose an intelligent reflecting surface (IRS)-assisted covert communication system (CCS) for communicating with a friendly unmanned aerial vehicle (UAV) in which the UAV generates artificial noise (AN) to interfere with monitoring. Furthermore, we model the power of AN emitted by the UAV using an uncertainty model, through which the closed-form detection error probability (DEP) of the covert wireless communication for monitoring is derived. Under the derived DEP, we formulate the optimization problem to maximize the covert rate, then design an iterative algorithm to solve the optimization problem and obtain the optimal covert rate using Dinkelbach method. Simulation results show that the proposed system achieves the maximum covert rate when the phase of the IRS units and the trajectory and transmit power of the UAV are optimized jointly.

As the technology scales down, two-dimensional (2D) NAND flash memory has reached its bottleneck. Three-dimensional (3D) NAND flash memory was proposed to further increase the storage capacity by vertically stacking multiple layers. However, the new architecture of 3D flash memory leads to new sources of errors, which severely affects the reliability of the system. In this paper, for the first time, we derive the channel probability density function of 3D NAND flash memory by taking major sources of errors. Based on the derived channel probability density function, the mutual information (MI) for 3D flash memory with multiple layers is derived and used as a metric to design the quantization. Specifically, we propose a dynamic programming algorithm to jointly optimize read-voltage thresholds for all layers by maximizing the MI (MMI). To further reduce the complexity, we develop an MI derivative (MID)-based method to obtain read-voltage thresholds for hard-decision decoding (HDD) of error correction codes (ECCs). Simulation results show that the performance with jointly optimized read-voltage thresholds can closely approach that with read-voltage thresholds optimized for each layer, with much less read latency. Moreover, the MID-based MMI quantizer almost achieves the optimal performance for HDD of ECCs.

Different from conventional reconfigurable intelligent surfaces (RIS), simultaneous transmitting and reflecting RIS (STAR-RIS) can reflect and transmit signals to the receiver. In this paper, to serve more ground users and increase deployment flexibility, we investigate an unmanned aerial vehicle (UAV) equipped with STAR-RIS (STAR-RIS-UAV)-aided wireless communications for multi-user networks. Energy splitting (ES) and mode switching (MS) protocols are considered to control the reflection and transmission coefficients of STAR-RIS elements. To maximize the sum rate of the STAR-RIS-UAV-aided coordinated multipoint (CoMP) cellular system for multi-user networks, the corresponding beamforming vectors as well as transmitted and reflected coefficient matrices are optimized. Specifically, instead of adopting the alternating optimization, we design an iteration method to optimize all variables for both the ES and MS protocols at the same time. Simulation results reveal that the STAR-RIS-UAV-aided CoMP system has a much higher sum rate than systems with conventional RIS or without RIS. Furthermore, the proposed structure is more flexible than fixed STAR-RIS and could greatly promote the sum rate.

Abstract As the technology of the Internet of Things (IoT) and mobile edge computing (MEC) develops, more and more tasks are offloaded to the edge servers to be computed. The offloading strategy performs an essential role in the progress of computation offloading. In a general scenario, the offloading strategy should consider enough factors, and the strategy should be made as quickly as possible. While most of the existing model only considers one or two factors, we investigated a model considering three targets and improved it by normalizing each target in the model to eliminate the influence of dimensions. Then, grey wolf optimizer (GWO) is introduced to solve the improved model. To obtain better performance, we proposed an algorithm hybrid whale optimization algorithm (WOA) with GWO named GWO-WOA. And the improved algorithm is tested on our model. Finally, the results obtained by GWO-WOA, GWO, WOA, particle swarm optimization (PSO), and genetic algorithm (GA) are discussed. The results have shown the advantages of GWO-WOA.

C15H20O2S, monoclinic, P21/c (No. 14), a = 12.8520(8) Å, b = 10.5864(8) Å, c = 11.2202(7) Å, β = 104.375(7)°, V = 1478.78(18) Å3, Z = 4, R gt(F) = 0.0655, wR ref(F 2) = 0.1781, T = 293 K.

Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children’s Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.

Recently, intelligent reflecting surfaces (IRSs) and unmanned aerial vehicles (UAVs) have been integrated into wireless communication systems to enhance the performance of air–ground transmission. To balance performance, cost, and power consumption well, a hybrid IRS and UAV-assisted directional modulation (DM) network is investigated in this paper in which the hybrid IRS consisted of passive and active reflecting elements. We aimed to maximize the achievable rate by jointly designing the beamforming and phase shift matrix (PSM) of the hybrid IRS subject to the power and unit-modulus constraints of passive IRS phase shifts. To solve the non-convex optimization problem, a high-performance scheme based on successive convex approximation and fractional programming (FP) called the maximal signal-to-noise ratio (SNR)-FP (Max-SNR-FP) is proposed. Given its high complexity, we propose a low-complexity maximal SNR-equal amplitude reflecting (EAR) (Max-SNR-EAR) scheme based on the maximal signal-to-leakage-noise ratio method, and the criteria of phase alignment and EAR. Given that the active and passive IRS phase shift matrices of both schemes are optimized separately, to investigate the effect of jointly optimizing them to improve the achievable rate, a maximal SNR majorization-minimization (MM) (Max-SNR-MM) scheme using the MM criterion to design the IRS PSM is proposed. Simulation results show that the rates harvested by the three proposed methods were slightly lower than those of the active IRS with higher power consumption, which were 35% higher than those of no IRS and random phase IRS, while passive IRS achieved only about a 17% rate gain over the latter. Moreover, compared with the Max-SNR-FP, the proposed Max-SNR-EAR and Max-SNR-MM methods caused obvious complexity degradation at the price of slight performance loss.

To provide rapid direction finding (DF) for unmanned aerial vehicle (UAV) emitters in future wireless networks, a low-complexity direction of arrival (DOA) estimation architecture for massive multiple-input multiple-output (MIMO) receiver arrays is constructed. In this paper, we propose two strategies to address the extremely high complexity caused by eigenvalue decomposition of the received signal covariance matrix. Firstly, a rapid power iterative rotational invariance (RPI-RI) method is proposed, which adopts the signal subspace generated by power iteration to obtain the final direction estimation through rotational invariance between subarrays. RPI-RI causes a significant complexity reduction at the cost of a substantial performance loss. In order to further reduce the complexity and provide good directional measurement results, the rapid power iterative polynomial rooting (RPI-PR) method is proposed, which utilizes the noise subspace combined with the polynomial solution method to obtain the optimal direction estimation. In addition, the influence of initial vector selection on convergence in the power iteration is analyzed, especially when the initial vector is orthogonal to the incident wave. Simulation results show that the two proposed methods outperform the conventional DOA estimation methods in terms of computational complexity. In particular, the RPI-PR method achieves more than two orders of magnitude lower complexity than conventional methods and achieves performance close to the Cramér–Rao Lower Bound (CRLB). Moreover, it is verified that the initial vector and the relative error have a significant impact on the performance with respect to the computational complexity.

The influence of the coupled electroencephalography (EEG) signal in electrooculography (EOG) on EOG-based automatic sleep staging has been ignored. Since the EOG and prefrontal EEG are collected at close range, it is not clear whether EEG couples in EOG or not, and whether or not the EOG signal can achieve good sleep staging results due to its intrinsic characteristics. In this paper, the effect of a coupled EEG signal in an EOG signal on automatic sleep staging is explored. The blind source separation algorithm was used to extract a clean prefrontal EEG signal. Then the raw EOG signal and clean prefrontal EEG signal were processed to obtain EOG signals coupled with different EEG signal contents. Afterwards, the coupled EOG signals were fed into a hierarchical neural network, including a convolutional neural network and recurrent neural network for automatic sleep staging. Finally, an exploration was performed using two public datasets and one clinical dataset. The results showed that using a coupled EOG signal could achieve an accuracy of 80.4%, 81.1%, and 78.9% for the three datasets, slightly better than the accuracy of sleep staging using the EOG signal without coupled EEG. Thus, an appropriate content of coupled EEG signal in an EOG signal improved the sleep staging results. This paper provides an experimental basis for sleep staging with EOG signals.

The angle of arrival (AOA) is widely used to locate a wireless signal emitter in unmanned aerial vehicle (UAV) localization. Compared with received signal strength (RSS) and time of arrival (TOA), AOA has higher accuracy and is not sensitive to the time synchronization of the distributed sensors. However, there are few works focusing on three-dimensional (3-D) scenarios. Furthermore, although the maximum likelihood estimator (MLE) has a relatively high performance, its computational complexity is ultra-high. Therefore, it is hard to employ it in practical applications. This paper proposed two center of inscribed sphere-based methods for 3-D AOA positioning via multiple UAVs. The first method could estimate the source position and angle measurement noise at the same time by seeking the center of an inscribed sphere, called the CIS. Firstly, every sensor measures two angles, the azimuth angle and the elevation angle. Based on that, two planes are constructed. Then, the estimated values of the source position and the angle noise are achieved by seeking the center and radius of the corresponding inscribed sphere. Deleting the estimation of the radius, the second algorithm, called MSD-LS, is born. It is not able to estimate angle noise but has lower computational complexity. Theoretical analysis and simulation results show that proposed methods could approach the Cramér–Rao lower bound (CRLB) and have lower complexity than the MLE.

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by detectors, we need to further confirm their number. Therefore, we perform feature extraction on the the eigenvalue sequence of a sample covariance matrix to construct a feature vector and innovatively propose a multi-layer neural network (ML-NN). Additionally, the support vector machine (SVM) and naive Bayesian classifier (NBC) are also designed. The simulation results show that the machine learning-based methods can achieve good results in signal classification, especially neural networks, which can always maintain the classification accuracy above 70% with the massive MIMO receive array. Finally, we analyze the classical signal classification methods, Akaike (AIC) and minimum description length (MDL). It is concluded that the two methods are not suitable for scenarios with massive MIMO arrays, and they also have much worse performance than machine learning-based classifiers.

This paper develops an unmanned aerial vehicle (UAV) deployment scheme in the context of the directional modulation-based secure precise wireless transmissions (SPWTs) to achieve more secure and more energy efficiency transmission, where the optimal UAV position for the SPWT is derived to maximize the secrecy rate (SR) without frequency diverse array (FDA) and injecting any artificial noise (AN) signaling. To be specific, the proposed scheme reveals that the optimal position of UAV for maximizing the SR performance has to be placed at the null space of Eves channel, which impels the received energy of the confidential message at the unintended receiver deteriorating to zero, whilst benefits the one at the intended receiver by achieving its maximum value. Moreover, the highly cost FDA structure is eliminated and transmit power is all allocated for transmitting a useful message which shows its energy efficiency. Finally, simulation results verify the optimality of our proposed scheme in terms of the achievable SR performance.

With an aim of maximizing the efficiency of edge offloading and the resource utilization of edge computing server simultaneously, a new flow-of-traffic prediction based edge computing offloading solution was proposed for Internet of vehicles (IoV).Firstly, both the efficiency utility function of vehicle and the resource utilization of mobile edge computing (MEC) server were established by considering task priority.Next, the formulated dual-objective optimization problem was transformed into a double auction problem between vehicles and MEC servers.Finally, based on the designed flow-of-traffic based pricing function of vehicle and MEC server, a McAfee auction algorithm was adopted to complete the edge computing process.Simulation results show that benefiting from the flow-of-traffic prediction information, the proposed solution can significantly improve both the efficiency of computation offloading and the utilization of computation resource.

Abstract In this paper, two regional robust secure precise wireless transmission (SPWT) schemes for multi-user unmanned aerial vehicle (UAV), (1)regional signal-to-leakage-and-noise ratio (SLNR) and artificial-noise-to-leakage-and-noise ratio (ANLNR) (R-SLNR-ANLNR) maximization and (2) point SLNR and ANLNR (P-SLNR-ANLNR) maximization, are proposed to tackle with the estimation errors of the target users’ location. In the SPWT system, the estimation error for SPWT cannot be ignored. However, the conventional robust methods in secure wireless communications optimize the beamforming vector in the desired positions only in statistical means and cannot guarantee the security for each symbol. The proposed regional robust schemes are designed for optimizing the secrecy performance in the whole error region around the estimated location. Specifically, with the known maximal estimation error, we define the target region and wiretap region. Then, we design an optimal beamforming vector and an artificial noise projection matrix, which achieve the confidential signal in the target area having the maximal power while only few signal power is conserved in the potential wiretap region. Instead of considering the statistical distributions of the estimated errors into optimization, we optimize the SLNR and ANLNR of the whole target area, which significantly decreases the complexity. Moreover, the proposed schemes can ensure that the desired users are located in the optimized region, which are more practical than the conventional methods. Simulation results show that our proposed regional robust SPWT design is capable of substantially improving the secrecy rate compared to the conventional non-robust method. The P-SLNR-ANLNR maximization-based method has the comparable secrecy performance with lower complexity than that of the R-SLNR-ANLNR maximization-based method.

For infants admitted at neonatal intensive care unit, the continuous monitoring of health parameters is critical for their optimal treatment and outcomes. So it's crucial to provide proper treatment, accurate and comfortable monitoring conditions for newborn infants. In this paper, we propose wearable sensor systems integrated with flexible material based non-invasive sensors for neonatal monitoring. The system aims at providing reliable vital signs monitoring as well as comfortable clinical environments for neonatal care. The system consists of a smart vest and a cloud platform. In the smart vest, a novel stretching sensor based on Polydimethylsiloxane-Graphene (PDMS-Graphene) compound is created to detect newborns' respiration signal; textile-based dry electrodes are developed to measure Electrocardiograph (ECG) signals; inertial measurement units (IMUs) are embedded to obtain movement information including accelerated speed and angular velocity of newborn wrists. Experiments were conducted to systematically test the sensing related characteristics of the aforementioned flexible materials and the performance of the proposed multi-sensor platform. The results show that the proposed system can achieve high quality signals. The wearable sensor platform is promising for continuous long term monitoring of neonates. The multi-modal physiological and behavioral signals measured by the platform can be further processed for clinical decision support on the neonatal health status.

The stepped-frequency (SF) waveform is highly sensitive to the target motion, which induces range shift and echo spread in a high-range-resolution profile (HRRP). This paper proposes a method based on a cross-transmitted double-stepped frequency (DSF) waveform and the phase-cancellation technique. The proposed method obtains the stationary HRRP of the moving targets according to the inverse discrete Fourier transform (IDFT) and complex multiplication. The results also show that the proposed method eliminates the generated false peaks from the existing methods. As a result, the obtained signal-to-noise ratio (SNR) of the HRRP using the proposed method is improved. Due to the cross-transmitted DSF waveform application, the proposed method adapts to higher speed targets. The analysis and simulation results validate the effectiveness of the proposed approach.

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method.

In this paper, we propose to use self-sustained jammers to generate artificial noise with the aim of enhancing the physical layer security of a directional modulation (DM) relay system. To this end, we specifically tackle an optimization problem of designing beamforming vectors at the source, relay, and jammers to maximize a lower bound on the average secrecy rate of the considered system. Due to that the optimization problem is non-convex and the optimization variables are coupled, and so we cannot directly solve this problem. As such, we first develop a centralized scheme to achieve the near-optimal beamforming vectors with the aid of semidefinite relaxation, successive convex approximation, and an alternating iterative algorithm. The tightness of the incurred relaxation and the convergence of the involved algorithm are proved. To reduce the overhead and complexity of the centralized scheme, we also develop two decentralized schemes with partial cooperation and without cooperation among devices to obtain suboptimal beamforming vectors. Our examination shows that the developed centralized scheme can achieve similar performance as the optimal beamforming vectors, which can only be obtained through exhaustive numerical search. Our examination also specifies the conditions under which the decentralized schemes can achieve similar or significantly worse performance relative to the centralized scheme. Furthermore, our examination demonstrates that the developed three schemes are robust to the angle estimation errors in practical DM systems.

Hybrid analog and digital (HAD) beamforming has been recently receiving considerable deserved attention for practical implementation on the large-scale antenna systems. Compared to fully-digital beamforming, partially-connected HAD beamforming can significantly reduce the hardware cost, complexity, and power consumption. This paper aims to mitigate interference and increase robustness against direction-of-arrival (DOA) mismatch along with lowering hardware complexity, cost, and power dissipation. To achieve these goals, a novel robust HAD beamforming receiver with partially-connected structure is proposed. It is based on methods of an improved bat algorithm (I-BA) and robust adaptive beamformers (RABs) in the digital domain. Since most of the RAB methods are sensitive to the DOA mismatch and depending on the complex weights which lead to an expensive receiver, the I-BA is proposed. In the analog part, analog phase alignment by linear searching (APALS) with sufficiently fine grid points is employed to optimize the analog beamforming matrix. The performance of the proposed I-BA is assessed using MATLAB simulation and compared with BA, and particle swarm optimization (PSO) algorithms. It shows better performance in terms of convergence speed, stability, and convergence rate. Besides, the proposed hybrid I-BA-APALS approach showed better performance compared to other proposed robust hybrid techniques, i.e., diagonal loading (DL) APALS (DL-APALS) and DL spatial matched filter (SMF) APALS (DL-SMF-APALS).

Intelligent transportation systems (ITSs), which can fulfill people's increasing requirements on mobility, have arisen as a popular research area. In order to implement the ITSs, a large amount of data needs to be executed within requested time. Due to the remote locations, cloud centers are no longer sufficient in data off-loading. A heterogeneous vehicular network, which consists of three kinds of computation resources, i.e., a cloud center, an edge node, and multiple vehicles, is, therefore, constructed to solve the aforementioned issues. First, considering the heterogeneity of each computation resource, the delay bound can be derived based on the martingale theory. Especially, data off-loading to the cloud center or to the edge node is assisted by road side units (RSUs). The communication link from the source node to the cloud center or to the edge node is modeled as a two-hop one. By utilizing the min-plus algebra, the two-hop link can be abstracted and analyzed in an equivalent single system further. Then, given a requested delay time, the maximum off-loading capacity of each resource can be obtained from the derived delay bound. When the tasks are smaller than the network available off-loading capacity, an optimal task allocation problem can, therefore, be constructed to minimize the overall delay violation probability. The numerical results are presented to show the performance of the proposed task allocation scheme in the heterogeneous vehicular networks. In addition, it can be verified that the proposed optimal task allocation scheme performs better than the benchmarks in the literature.

Security has become an extremely important research topic in wireless networks over the last decade, as it is intimately related to both individual privacy and national security. Directional modulation, as a conventional type of secure modulations, transmits confidential information along the desired directions of legitimate receivers, and artificial noise in other directions, to deliberately confuse eavesdroppers in line-of-sight channels. Recently, artificial noise is also introduced into spatial modulation, leading to a secure spatial modulation strategy. In this Special Section in IEEE A CCESS, secure modulation is defined broadly as any secure modulation method, which includes, but is not limited to, secure directional modulation, secure spatial modulation, and secure index modulation.

Millimeter wave (mmWave) communication has been regarded as one of the most promising technologies for the future generation wireless networks because of its advantages of providing a ultra-wide new spectrum and ultra-high data transmission rate. To reduce the power consumption and circuit cost for mmWave systems, hybrid digital and analog (HDA) architecture is preferred in such a scenario. In this paper, an artificial noise (AN) aided secure HDA beamforming scheme is proposed for mmWave multiple input single output (MISO) system with low resolution digital-to-analog converters (DACs) and finite-quantized phase shifters on radio frequency. The additive quantization noise model for AN aided HDA system is established to make an analysis of the secrecy performance of such systems. With the partial channel knowledge of eavesdropper available, an approximate expression of secrecy rate (SR) is derived. Then using this approximation formula, we propose a two-layer alternatively iterative structure (TLAIS) for optimizing digital precoder (DP) of confidential message (CM), digital AN projection matrix (DANPM) and analog precoder (AP). The inner-layer iteration loop is to design the DP of CMs and DANPM alternatively given a fixed matrix of AP. The outer-layer iteration loop is between digital baseband part and analog part, where the former refers to DP and DANPM, and the latter is AP. Then for a given digital part, we propose a gradient ascent algorithm to find the AP vector. Given a matrix of AP, we make use of general power iteration (GPI) method to compute DP and DANPM. This process is repeated until the terminal condition is reached. Simulation results show that the proposed TLAIS can achieve a better SR performance than existing methods, especially in the high signal-to-noise ratio region.

The radio frequency (RF) phase shifter with finite quantization bits in analog beamforming (AB) structure forms quantization error (QE) and causes a performance loss of received signal to interference plus noise ratio (SINR) at the receiver (called Bob). By using the law of large numbers in probability theory, the closed-form expression of the SINR performance loss is derived to be inversely proportional to the square of Sinc (or sin(x)/x) function. Here, a phase alignment method is applied in the directional modulation transmitter with the AB structure. Also, the secrecy rate (SR) expression is derived with the QE. From the numerical simulation results, we find that the SINR performance loss gradually decreases as the number L of quantization bits increases. This loss is less than 0.3 dB when L is larger than or equal to three. As L exceeds five, the SINR performance loss at Bob can be approximately trivial. Similarly, the SR performance loss gradually reduces as L increases. In particular, the SR performance loss is about 0.1 bits/s/Hz for L = 3 at signal-to-noise ratio of 15 dB.

In this paper, given the beamforming vector of confidential messages and the artificial noise (AN) projection matrix and total power constraint, a power allocation(PA) strategy of maximizing secrecy rate (Max-SR) is proposed for secure directional modulation networks. By the method of Lagrange multiplier, the analytical expression of the proposed PA strategy is derived. To confirm the benefit from the Max SR-based PA strategy, we take all the null-space projection beamforming scheme as an example and derive its closed-form expression of optimal PA strategy. From simulation results, we find the following facts: in the medium and high signal-to-noise ratio (SNR) regions. compared with three typical PA parameters such β = 0.1, 0.5, and 0.9, the optimal PA shows a substantial SR performance gain with maximum gain percent up to more than 60%. In addition, as the PA factor increases from 0 to 1, the achievable SR increase accordingly in the low SNR region, whereas it first increases and the decreases in the medium and high SNR regions, where the SR can be approximately viewed as a convex function of the PA factor. Finally, as the number of antennas increases, the optimal PA factor become large and tends to one in the medium and high SNR regions. In other words, the contribution of AN to SR can be trivial in such a situation.

Nonconvex penalties have recently received considerable attention in sparse recovery based on Gaussian assumptions. However, many sparse recovery problems occur in the presence of impulsive noises. This paper is concerned with the analysis and comparison of different sparsity-inducing penalties for L1-loss function-based robust sparse recovery. To solve these nonconvex and nonsmooth optimization problems, we use the alternating direction method of multipliers framework to split this difficult problem into tractable sub-problems in combination with corresponding iterative proximal operators. This paper employs different nonconvex penalties and compares the performances, advantages, and properties and provides guidance for the choice of the best regularizer for sparse recovery with different levels of impulsive noise. Experimental results indicate that convex lasso (L1-norm) penalty is more effective for the suppression of highly impulsive noise than nonconvex penalties, while the nonconvex penalties show the potential to improve the performance in low and medium level noise. Moreover, among these nonconvex penalties, Lp norm can often obtain better recovery performance.

Power line communication (PLC) is an attractive approach to provide information transfer services for the smart home. The PLC network inherently suffers from the severe channel fading and impulsive noise. According to the mode of impulsive and the impulsive occurrence, the PLC channel can be modeled as the channel with memory. We compute the achievable rate by employing the Markov process with a finite number of states based on the dependence among the impulsive occurrence numbers in different time slots. Furthermore, the cumulative distribution function (CDF) for the received signal-to-noise ratio is derived with the statistical characteristics of the PLC channel. This helps in obtaining the ergodic capacity and outage probability for the PLC channels. We finally present the simulation results to verify the analytical results.

In a directional modulation network, a general power iterative (GPI) based beamforming scheme is proposed to maximize the secrecy rate (SR), where there are two optimization variables required to be optimized. The first one is the useful precoding vector of transmitting confidential messages to the desired user while the second one is the artificial noise (AN) projection matrix of forcing more AN to eavesdroppers. In such a secure network, the paramount problem is how to design or optimize the two optimization variables by different criteria. To maximize the SR (Max-SR), an alternatively iterative structure (AIS) is established between the AN projection matrix and the precoding vector for confidential messages. To choose a good initial value of iteration process of GPI, the proposed Max-SR method can readily double its convergence speed compared to the random choice of initial value. With only four iterations, it may rapidly converge to its rate ceil. From simulation results, it follows that the SR performance of the proposed AIS of GPI-based Max-SR is much better than those of conventional leakage-based and null-space projection methods in the medium and large signal-to-noise ratio (SNR) regions, and its achievable SR performance gain gradually increases as SNR increases.

In this paper, a random frequency diverse array-based directional modulation with artificial noise (RFDA-DM-AN) scheme is proposed to enhance physical layer security of wireless communications. Specifically, we first design the RFDA-DM-AN scheme by randomly allocating frequencies to transmit antennas, thereby achieving 2-D (i.e., angle and range) secure transmissions, and outperforming the state-of-the-art 1-D (i.e., angle) phase array (PA)-based DM scheme. Then we derive the closed-form expression of a lower bound on the ergodic secrecy capacity (ESC) of our RFDA-DM-AN scheme. Based on the theoretical lower bound derived, we further optimize the transmission power allocation between the useful signal and artificial noise (AN) in order to improve the ESC. Simulation results show that: (1) our RFDA-DM-AN scheme achieves a higher secrecy capacity than that of the PA-based DM scheme; (2) the lower bound derived is shown to approach the ESC as the number of transmit antennas N increases and precisely matches the ESC when N is sufficiently large; and (3) the proposed optimum power allocation achieves the highest ESC of all power allocations schemes in the RFDA-DM-AN.

Small-cell caching utilizes the embedded storage of small-cell base stations (SBS) to cache popular network contents, for the purpose of reducing duplicate transmissions in mobile networks and offloading the data traffic from macro-cell base stations. In this paper, we propose a random small-cell caching system, where each SBS randomly caches a subset of popular contents with a specified caching probability. We particularly focus on the probability that mobile users can successfully download their requested files from the SBSs, namely, successfully downloading probability (SDP). A sophisticated path-loss model incorporating both line-of-sight (LoS) and non-LoS (NLoS) transmissions is introduced into the SDP analysis. By modeling the distribution of the SBSs as a Poisson point process, we develop theoretical results of the SDP performance based on stochastic geometry theory. Additionally, we investigate the impacts of the parameters of the SBSs, i.e., transmission power and deployment intensity, on the SDP. Monte Carlo simulations show the consistency with our derived SDP. Also, numerical results validate our analysis on the related parameters and their impacts on the SDP performance.

In orthogonal frequency division multiplexing relay networks with in-phase and quadrature imbalances and full-duplex relay station (RS), how to optimize pilot pattern and power allocation using the criterion of minimizing the sum of mean square errors (Sum-MSE) for the frequency-domain least-squares channel estimator has a heavy impact on self-interference (SI) cancellation. First, the design problem of pilot pattern is casted as a convex optimization. From the Karush-Kuhn-Tucker conditions, the optimal analytical expression is derived when source and RS powers are given or fixed. Second, under the total transmit power sum constraint of source node and RS, an optimal power allocation (OPA) strategy is proposed to further alleviate the effect of Sum-MSE. Simulation results show that the proposed OPA performs better than equal power allocation (EPA) in terms of Sum-MSE, and the Sum-MSE performance gain increases with deviating ρ from the value of ρo minimizing the Sum-MSE, where ρ is defined as the average ratio of the residual SI channel gain at RS to the intended channel gain from source to RS. For example, the OPA achieves approximately 5-dB signal-to-noise ratio gain over EPA by shrinking or stretching ρ with a factor 4. More importantly, the more ρ decreases or increases, the more the performance gain becomes significant.

This paper proposes a carrier-sensing multiple access protocol for asynchronous multiple-packet reception (MPR), which enables wireless receivers to correctly decode partially time-overlapping packets. Unlike previous studies, which require back-off nodes to constantly monitor the channel, our design requires only that each node sense the number of transmitting nodes after the completion of the back-off period for determining whether to begin transmission in the next time slot. In addition, we develop an analytical model to evaluate relevant parameters and performance metrics of the proposed protocol. Our model relies on the channel-sensing probability of a node in a randomly chosen slot, rather than channel-accessing probability adopted in previous models for asynchronous MPR. The results are validated through numerical study under a variety of network conditions. We also show that the proposed protocol is quite robust to imperfect estimation in channel sensing, and is more energy-efficient than other similar threshold-based protocols.

Computation offloading is one of the most important problems in edge computing. Devices can transmit computation tasks to servers to be executed through computation offloading. However, not all the computation tasks can be offloaded to servers with the limitation of network conditions. Therefore, it is very important to decide quickly how many tasks should be executed on servers and how many should be executed locally. Only computation tasks that are properly offloaded can improve the Quality of Service (QoS). Some existing methods only focus on a single objection, and of the others some have high computational complexity. There still have no method that could balance the targets and complexity for universal application. In this study, a Multi-Objective Whale Optimization Algorithm (MOWOA) based on time and energy consumption is proposed to solve the optimal offloading mechanism of computation offloading in mobile edge computing. It is the first time that MOWOA has been applied in this area. For improving the quality of the solution set, crowding degrees are introduced and all solutions are sorted by crowding degrees. Additionally, an improved MOWOA (MOWOA2) by using the gravity reference point method is proposed to obtain better diversity of the solution set. Compared with some typical approaches, such as the Grid-Based Evolutionary Algorithm (GrEA), Cluster-Gradient-based Artificial Immune System Algorithm (CGbAIS), Non-dominated Sorting Genetic Algorithm III (NSGA-III), etc., the MOWOA2 performs better in terms of the quality of the final solutions.