A methodology to benchmark flexible payload architectures in a megaconstellation use case

This paper proposes a methodology to benchmark satellite payload architectures and find the optimal trade‐offs between high flexibility and low complexity. High flexibility would enable the satellite to adapt to various distributions of user terminals on the ground and fulfill the data rate demand of these users. Besides, low complexity is required to keep satellite networks competitive in the context of emerging 5G networks. To estimate the flexibility of a payload, an indicator to characterize the non‐uniformity of user distributions is proposed. Each benchmarked payload may be characterized by a graph relating the throughput to this parameter further denoted . The payload provides the same throughput trends for different scenarios of user distributions with the same parameter. As a consequence, the average capacity of the system may be estimated by (a) calculating the probability distribution of over the orbit and (b) integrating the throughput based on this payload response. It thus results in a straightforward way for benchmarking payloads directly on an estimation of the averaged capacity, accounting for the user distribution over the earth. A simulation platform has been developed to characterize the payload throughput including the implementation of a resource allocation algorithm that accounts for constraints of various payloads. Using this definition and the developed tool, we benchmark a bent‐pipe architecture, a beam hopping architecture and a hybrid beam‐steering architecture for a LEO megaconstellation use case. The methodology showcases the interest for investigating different payload architectures depending on realistic traffic scenario analysis.     

Performance analysis of satellite link using Gaussian mixture model under rain

The evolution of communication systems to the next generation, for example, B5G and 6G, demands an ultrareliable performance regardless of weather conditions. Such ultrareliable system design will require that the effects of adverse weather events on the communication system have to be computed more accurately so that physical layer compensation should be optimally and dynamically adaptive to such events. The performance of satellite links is severely affected by dynamic rain attenuation, and thus, accurate and reliable modeling of performance parameters is essential for dynamic fade countermeasures, especially above 10 GHz. In this work, we model the energy per bit to noise spectral density ratio ( $E_b/N_0$) using Gaussian mixture (GM) model during rainy events. The developed mathematical expression is used to accurately model the average $E_b/N_0$, bit error rate (BER), outage probability, and ergodic channel capacity of the link. The average BER, upper bound on BER, and average ergodic capacity of an M-ary phase shift keying scheme (MPSK) using the GM model of $E_b/N_0$ are derived to evaluate the performance of the link under such weather impairments. We then show the numerical results and analysis using the GM model of the measured $E_b/N_0$ data obtained with the AMoS-7 satellite at a site located in Israel.     

Background memory-assisted zero-shot video object segmentation for unmanned aerial and ground vehicles

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean $J$ and $F$ values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.     

A deep learning based latency aware predictive routing model for network-on-chip architectures

Network on Chip (NoC) is an evolving platform for communications related applications, which are executed on a single silicon chip. There are several routing models in NoC architectures, but the accuracy of these models is limited, and the existing models are degraded because of over and under fitting issues. This research introduces the new deep learning-based latency aware predictive routing model for on-chip networks to route packets with better performance and power efficiency. The deep learning model used in this research is a new convolutional residual gated recurrent unit (CRGRU) with queuing theory. Moreover, the source and channel queuing delay is comprised of features to learn spatial and sequential information that improves the overall prediction accuracy. This router is modified by the intrusion of the Router States Monitor unit and the CRGRU hardware engine. The work is executed using the Xilinx platform, and the performance measures like latency and throughput are obtained by varying the network size as $4\times 4$, $8\times 8$, and $12\times 12$ and also varying the buffer space and length as $L=4,B=9$, $L=9,B=4$, and $L=14,B=3$, respectively. In addition, the squared correlation coefficient (SCC) and normalized root mean square error (NRMSE) are evaluated and compared with existing learning models to validate the proposed model.     

基于BP和RBF神经网络的C-V2X无线资源管理

为了提升蜂窝车联网（Cellular Vehicle-to-Everything,C-V2X）资源复用的有效性和降低终端间的干扰，提出通过神经网络对未来时刻车流量的预测辅助无线资源管理方案。依据车载单元（On Board Unit,OBU）与路侧单元（Road Side Unit,RSU）间的车联网消息，获取RSU覆盖区域内各时刻的车流情况，分别采用BP（Back Propagation）神经网络和RBF（Radial Basis Function）神经网络进行短时交通流预测。RSU根据预测结果进行自适应分簇，簇间复用相同资源，簇内进行资源池的划分，RSU覆盖内的OBU在划分的资源池中选择发送资源，从而减少终端间的干扰，并保证热点区域车辆拥有更多的资源。仿真结果表明，在道路交通拥塞的场景下，所提方案的数据包接收率较标准中的方案提升14%，较典型文献方案提升10%，保证了通信的可靠性。     

Joint routing,link capacity dimensioning,and switch port optimization for dynamic traffic in optical networks

This paper considers a challenging problem: to simultaneously optimize the cost and the quality of service in opaque wavelength division multiplexing (WDM) networks. An optimization problem is proposed that takes the information including network topology, traffic between end nodes, and the target level of congestion at each link/node in WDM networks. The outputs of this problem include routing, link channel capacities, and the optimum number of switch ports locally added/dropped at all switch nodes. The total network cost is reduced to maintain a minimum congestion level on all links, which provides an efficient trade-off solution for the network design problem. The optimal information is utilized for dynamic traffic in WDM networks, which is shown to achieve the desired performance with the guaranteed quality of service in different networks. It was found that for an average link blocking probability equal to 0.015, the proposed model achieves a net channel gain in terms of wavelength channels ( ) equal to 35.72 , 39.09 , and 36.93 compared to shortest path first routing and equal to 29.41 , 37.35 , and 27.47 compared to alternate routing in three different networks.     

A prototype of high‐sensitivity noncoherent receiver for ADS‐B signals

In this paper, we design a high‐sensitivity noncoherent receiver for the reception of automatic dependent surveillance‐broadcast (ADS‐B) signals in a satellite‐based application. The proposed receiver is of constant false alarm rate (CFAR) property and implemented completely by the field programmable gate array (FPGA). Two vital parameters of the receiver, probabilities of detection and correct reception, are analytically formulated and validated by Monte Carlo simulations as well. We examine the availability of algorithms exploited in the proposed receiver by feeding the real ADS‐B signals collected near the Nanjing Lukou International Airport. Then, we build a receiver prototype. Some prototype tests are carried out in the laboratory. We find out that the sensitivity of the proposed receiver is approximately  dBm, corresponding to the probability of correct reception equal to 0.93, and it is consistent with the theoretical value.     

Path loss model based on exponential water cycle algorithm for wireless sensor network

Wireless sensor networks (WSNs) are frequently employed in the agriculture field to improve the quality and crop yield. The WSN might reduce the quality of the communication link because of the absorption, dispersion, and attenuation through the leaves of plants. Therefore, estimating the path loss due to signal attenuation before WSN deployment is crucial for the smooth operation of the network. In this research paper, three innovative path loss models are defined based on the MATLAB curve fitting tool: polynomial water cycle (PWC), exponential water cycle (EWC), and Gaussian water cycle (GWC) algorithm. Here, the path loss between the router node and the coordinator node is modeled on the basis of the received signal strength indicator (RSSI) and time of arrival (TOA) measurements in a sugarcane field. The correlation coefficient between the RSSI measurement and the distance must be increased to create a precise path loss model. This paper integrates the exponential, polynomial, and Gaussian functions with the water cycle algorithm (WCA) to evaluate the optimal coefficients that would lead to precise path loss models. The performance of the proposed models that determines the optimum linear fit between RSSI and distance is validated using the correction coefficient $R^2$. The results show that the proposed path loss model is superior to existing path loss models. The correlation coefficient $R^2$ of the proposed EWC model is 0.9993, whereas the existing PE-PSO, LNSM, and PSO-Exponential models yield 0.98, 0.87, and 0.93, respectively. Also, the proposed models attain the best mean absolute error (MAE) of 0.2187, 0.2951, and 0.3457 dBm for EWC, PWC, and GWC algorithms, respectively.     

An eight-element sequentially rotated crescent-slot-coupled dielectric resonator antenna array for WIFI applications

In this communication, the author presents an eight-element sequentially rotated (SR) circularly polarized (CP) dielectric resonator antenna (DRA) for operation in the IEEE 802.11a standard. A novel resonating element composed of a crescent slot (CS) used to excite a rectangular dielectric resonator (RDR) is proposed that has two orthogonal modes ${\mathit{TE}}_{1\delta 1}^y$ and ${\mathit{TE}}_{\delta 21}^x$ as required for CP radiation. An SR series–parallel geometry is used to prototype the array feed network to allocate the array elements to symmetrical positions. The phase progression of each element was 45° along the array, and the signal magnitude was distributed evenly based on the binomial theory to enhance the antenna performance. The prototyped SR array had a size of $46\times 46\times 0.813$ mm³ and was measured and characterized in order to authenticate the design. The resonance bandwidth (S₁₁ ˂ −10 dB) was found to be 14.28% with a 3 dB axial ratio (AR) of 17.7% for right-hand CP. The gain varied from 15.71 to 16.26 dBi within the operating band. The size, gain, and impedance bandwidth of the proposed array make it a potential candidate for devices operating in the IEEE 802.11a band.     

Performance analysis of intelligent reflecting surface-assisted mobile wireless networks subjected to generalized Gaussian noise

Different from the prior works, this paper presents the performance analysis of an intelligent reflecting surface (IRS)-assisted communication link in a static and mobile scenario impaired by Rayleigh fading and additive white generalized Gaussian noise (AWGGN). Precisely, the IRS is configured as an intelligent access point, and the mobile behavior of the nodes is characterized by the random waypoint (RWP) model. To this end, closed-form expressions of average bit error rate (BER), average channel capacity (ACC), and outage probability (OP) in both static and mobile scenarios are obtained. To gain further insight into the system performance at high signal-to-noise ratio (SNR), asymptotic expressions are obtained. Moreover, the effect of the number of reflective elements ( $N$) and the shaping parameter ( $\lambda$) on the system performance is thoroughly studied. The results indicate that introduction of IRS leads to significant improvement in the overall system performance. The derived results are corroborated with Monte Carlo simulations.     

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

With the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min-Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.     

2D Porous Polymers with sp2‐Carbon Connections and Sole sp2‐Carbon Skeletons

2D porous polymers with a planar architecture and high specific surface area have significant applications potential, such as for photocatalysis, electrochemical catalysis, gas storage and separation, and sensing. Such 2D porous polymers have generally been classified as 2D metal–organic frameworks, 2D covalent organic frameworks, graphitic carbon nitride, graphdiyne, and sandwich‐like porous polymer nanosheets. Among these, 2D porous polymers with sp²‐hybridized carbon ( $C_{s{p}^2}$) bonding are an emerging field of interest. Compared with 2D porous polymers linked by B? O, C?N, or C?C bonds, $C_{s{p}^2}$‐linked 2D porous polymers exhibit extended electron delocalization resulting in unique optical/electrical properties, as well as high chemical/photostability and tunable electrochemical performance. Furthermore, such 2D porous polymers are one of the best precursors for the fabrication of 2D porous carbon materials and carbon skeletons with atomically dispersed transition‐metal active sites. Herein, rational synthetic approaches for 2D porous polymers with $C_{s{p}^2}$ bonding are summarized. Their current practical photoelectric applications, including for gas separation, luminescent sensing and imaging, electrodes for batteries and supercapacitors, and photocatalysis are also discussed.     

移动Ad Hoc网中资源估计及预测的研究

为了在Ad Hoc网络中支持实时、多媒体等业务,必须要求该网络能够提供QoS保证,即满足实时业务所需的时延和吞吐量要求.资源估计和质量预测是提供QoS保证的一个重要环节.对近年来国内外在无线AdHoc网络中资源估计和预测方面取得的研究成果作出了全面的概括总结和比较分析,系统阐述了在无线Ad Hoc网络中资源估计的方法和应用.指出了亟待解决的问题和今后的研究方向.     

Anisotropic Phonon Response of Few‐Layer PdSe2 under Uniaxial Strain

PdSe₂, an emerging 2D material with a novel anisotropic puckered pentagonal structure, has attracted growing interest due to its layer‐dependent electronic bandgap, high carrier mobility, and good air stability. Herein, a detailed Raman spectroscopic study of few‐layer PdSe₂ (two to five layers) under the in‐plane uniaxial tensile strain up to 3.33% is performed. Two of the prominent PdSe₂ Raman peaks are influenced differently depending on the direction of strain application. The $A_g^1$ mode redshifts more than the $A_g^3$ mode when the strain is applied along the a‐axis of the crystal, while the $A_g^3$ mode redshifts more than the $A_g^1$ mode when the strain is applied along the b‐axis. Such an anisotropic phonon response to strain indicates directionally dependent mechanical and thermal properties of PdSe₂ and also allows the identification of the crystal axes. The results are further supported using first‐principles density‐functional theory. Interestingly, the near‐zero Poisson’s ratios for few‐layer PdSe₂ are found, suggesting that the uniaxial tensile strain can easily be applied to few‐layer PdSe₂ without significantly altering their dimensions at the perpendicular directions, which is a major contributing factor to the observed distinct phonon behavior. The findings pave the way for further development of 2D PdSe₂‐based flexible electronics.     

The approach of hybrid adaptive coupling synchronization among N Lorenz systems and its application in secure communication for multi-unmanned aerial vehicle formation

In this paper, it is proposed that a hybrid global adaptive coupling synchronization scheme among $N$ Lorenz chaotic dynamical nodes to realize the secure communication system between base station and multi-unmanned aerial vehicle (UAV) formation. The specific method is that the feedback drive–response synchronization is utilized for first two nodes of base station and the leader of multi-UAV formation, and the nodes of all UAVs are coupled by unidirectional adaptive coupling synchronization according to a directed link in ad hoc network of multi-UAV formation. It is demonstrated that the asymptotic stability of the proposed hybrid adaptive coupled synchronization by constructing the Lyapunov function. In this way, the encrypted information formed by plaintext information masked into the chaotic sequence generated by the chaotic dynamical node of base station; meanwhile, it is fed back into the base station node as the drive system. On the other hand, encrypted information is forwarded to the leader node as the response system for decryption. The feedback driver–response synchronization is used to realize secure communication between the base station and the leader of multi-UAV formation. Meanwhile, secure communication among its leader and followers is achieved through the unidirectional adaptive coupling synchronization in the network. This strategy ensures the multi-UAV formation decrypting encrypted information synchronously and effectively improves the security, consistency, and overall performance of their commands.     

Macroscopic and Microscopic Structures of Cesium Lead Iodide Perovskite from Atomistic Simulations

A first‐principles‐based effective Hamiltonian is developed and employed to investigate finite‐temperature structural properties of a prototype of perovskite halides, that is CsPbI₃. Such simulations, when using first‐principles‐extracted coefficients, successfully reproduce the existence of an orthorhombic Pnma state and its iodine octahedral tilting angles around room temperature. However, they also yield a direct transformation from Pnma to cubic $Pm\overline{3}m$ upon heating, unlike measurements that reported the occurrence of an intermediate long‐range‐tilted tetragonal P4/mbm phase in‐between the orthorhombic and cubic phases. Such disagreement, which may cast some doubts about the extent to which first‐principle methods can be trusted to mimic hybrid perovskites, can be resolved by “only” changing one short‐range tilting parameter in the whole set of effective Hamiltonian coefficients. In such a case, some reasonable values of this specific parameter result in the predictions that i) the intermediate P4/mbm state originates from fluctuations over many different tilted states; and ii) the cubic $Pm\overline{3}m$ phase is highly locally distorted and develops strong transverse antiphase correlation between first‐nearest neighbor iodine octahedral tiltings, before undergoing a phase transition to P4/mbm under cooling.     

Spectrum sensing performance of wireless cognitive radio sensor network with hard-decision fusion over generalized α−μ fading channels

The analysis of spectrum sensing performance of energy detection (ED)-based wireless cognitive radio sensor network (ED-WCRSN) with hard-decision combining (HDC) is presented in this paper. Particularly, several network parameters are derived to estimate the performance of ED-WCRSN, considering channel errors, noise, and generalized $\alpha -\mu$ fading. In the considered network, first, cognitive radio sensor (CRS) senses a primary user (PU), gets sensing data, and then uses an ED to make a local binary decisions about PU's active or inactive status. In both sensing and reporting channels, the channel error probability is also taken into account. Next, HDC technique is used at control center (CC) to combine the locally obtained decisions, and a final decision about the status of the PU is made. To do so, first, the expression for the novel and analytical, which incorporates noise and $\alpha -\mu$ fading, detection probability in a CRS is derived and validated using Monte Carlo simulations in MATLAB and using an experimental setup. Then, utilizing derived mathematical expressions, closed-form expressions of an average error rate (AER), optimal numbers of CRSs, and detection thresholds under noisy and $\alpha -\mu$ fading conditions are developed. The substantial influence of channel and network factors is assessed using receiver operating characteristics (ROC), complementary ROC, and AER. Finally, the impacts of channel and network parameters on ED-WCRSN performance are explored. For numerous parameters of the considered network, the optimal values detection threshold and number of CRSs are also found.     

Variational autoencoder for prosody-based speaker recognition

This paper describes a novel end-to-end deep generative model-based speaker recognition system using prosodic features. The usefulness of variational autoencoders (VAE) in learning the speaker-specific prosody representations for the speaker recognition task is examined herein for the first time. The speech signal is first automatically segmented into syllable-like units using vowel onset points (VOP) and energy valleys. Prosodic features, such as the dynamics of duration, energy, and fundamental frequency ( $F_0$), are then extracted at the syllable level and used to train/adapt a speaker-dependent VAE from a universal VAE. The initial comparative studies on VAEs and traditional autoencoders (AE) suggest that the former can efficiently learn speaker representations. Investigations on the impact of gender information in speaker recognition also point out that gender-dependent impostor banks lead to higher accuracies. Finally, the evaluation on the NIST SRE 2010 dataset demonstrates the usefulness of the proposed approach for speaker recognition.