Low Delay Research Articles

A 3D LEO Channel Model Based on GBSM for Satellite‐Ground Communication Scenario

ABSTRACTLow earth orbit (LEO) satellites have the characteristics of low communication delay, low deployment cost, and wide coverage, which have become an important component of the 6G air‐space‐ground integrated information network. However, satellite‐ground communication has a large propagation distance, complex fading, and fast terminal movement speed, causing the channel characteristics different from terrestrial communication networks. Therefore, channel modeling is necessary when deploying a satellite‐ground communication network. In this paper, a 3D geometry‐based stochastic model (GBSM) is proposed for satellite‐ground communication links. The proposed channel model includes several environments such as urban, suburban, and rural. Based on this model, the channel impulse response (CIR) can be obtained, and the closed‐form expression of spatial‐temporal correlation function and Doppler power spectrum density are derived. Through simulation, the characteristics of large‐scale fading and small‐scale fading are analyzed, which depict the significant differences from the terrestrial networks. The relevant results can provide contributions to the design of future satellite‐ground communication systems.

Adaptive Random Early Detection Using Deep Reinforcement Learning for Enhanced Congestion Control in Dynamic TCP/IP Networks

AQM is used to meet targets for congestion control and low delay, high throughput in TCP/IP networks. But methods developed for AQM like Random Early Detection (RED) work strictly on the control parameters pre-set on them and thus may not respond very well to changing network conditions. Thus, this paper presents a composite AQM model using DRL based on DQN, and the ability to learn independently regarding the AQM weight parameter of the queue. The fact that DRL is adaptive means the effectiveness of the proposed system is also assured. As it is observed in implementing traditional model-based approach, the stability and performance degrade when tested in different network conditions; however, DRL’s ability to learn independently enables ML to solve network congestion as it happens. Consequently, more stability is achieved, the delay is lesser, more bandwidth is used and the overall packet drop rate is low; the proposed DRL-RED model is ideal for dynamic network environments. The proposed DRL-RED model is compared with the regular RED algorithm for both low density and high-density networks. This proposed model has achieved sustained throughput of up to 49.9 Mbps with 0.949 % reduction on delay and very low PLR of 8.38043%. From the comparative analysis, it is clear that the employment of DRL with RED improves the stock of network throughput, the reduction on packet drop frequency, and the general delay in network situations. There are two main disadvantages of this approach: first, it cannot reach the heavy-traffic, the problem partly solved by model-based approach; second, the values of the congestion-control parameters cannot be reset, an issue eradicated by DRL as a result of its inherent adaptiveness. Consequently, new stability and increased performance can be achieved under various network conditions.

Hierarchical federated deep reinforcement learning based joint communication and computation for UAV situation awareness

The computation-intensive situational awareness (SA) task of unmanned aerial vehicle (UAV) is greatly affected by its limited power and computing capability. To solve this challenge, we consider the joint communication and computation (JCC) design for UAV network in this paper. Firstly, a multi-objective optimization (MOO) model, which can optimize UAV computation offloading, transmit power, and local computation resources simultaneously, is built to minimize energy consumption and task execution delay. Then, we develop Thompson sampling based double-DQN (TDDQN) learning algorithm which allows the agent to explore more deeply and effectively, and propose a joint optimization algorithm that combines TDDQN and sequential least squares quadratic programming (SLSQP) to handle the MOO problem. Finally, to enhance the training speed and quality, we incorporate federated learning (FL) into the presented joint optimization algorithm and propose hierarchical federated TDDQN with SLSQP (HF TDDQN-S) to implement the JCC design. Simulation results show that the introduced HF TDDQN-S can efficiently learn the best JCC strategy and minimize the average cost contrasted with the DDQN with SLSQP (DDQN-S) and TDDQN with SLSPQ (TDDQN-S) approach, and achieve the low average delay SA with power efficient.

Integrating Smart Computility for Subflow Orchestration in Remote Virtual Services.

The burgeoning domain of the metaverse has sparked significant interest from a diverse array of industries, including healthcare services. However, the metaverse and its associated applications present various challenges to existing networks. First, to meet the increasing demands of the metaverse, there is a need for enhanced bandwidth, reduced latency, and improved packet loss control. Furthermore, the transmission mechanism should exhibit flexibility to automatically adapt to the diverse hybrid needs of different healthcare services. In this article, a multipath transmission-based paradigm tailored for the metaverse-based healthcare services is developed. Significantly, we devise an orchestration framework to reconcile edge-side subflow management with diverse healthcare applications. Using machine learning techniques, the framework can produce near-optimal subflow adjustment strategies for client nodes and miscellaneous services. Comprehensive experiments are performed on applications with diverse requirements to validate the adaptability of the framework to the application needs. The experimental results demonstrate that the proposed method enables the network to autonomously adapt to changing network conditions and service requirements. This includes applications' preferences for high throughput, low delay, and high stability. Moreover, the test results show that the proposed approach can notably decrease the occurrences of network quality falling below the minimum requirement. Given its adaptability and impact on network quality, this work paves the way for future metaverse-based healthcare services.

Wideband Air‐Filled Coaxial Filter Line

ABSTRACTA design for manufacturing (DFM) of a well‐performing wideband coaxial filter line using direct metal laser sintering (DMLS) additive manufacturing is proposed. The demonstrated transmission line is an air‐filled coaxial line chosen for low loss and consistent group delay over the bandwidth. Transmission line theory is used first to account for the impact of the shorted stub needed to support the central conductor. Deviations due to parasitics are described to transition the design from an ideal model to a physical prototype. The constraints inherent in DMLS are discussed and accounted for throughout the DFM. This process is demonstrated with a prototype having return and insertion losses of greater than 10 dB and less than 1.64 dB, respectively, over a 5.3:1 bandwidth from 6.4 to 33.9 GHz with a flat group delay of 129 ps ± 7%.

Study of A Heterojunction Double Gate Ferroelectric p-n-i-n Tunnel FET combining analytical modeling and TCAD simulation

A short channel Heterojunction Double Gate Ferroelectric p-n-i-n Tunnel Field Effect Transistor structure is proposed in this article to alleviate undesirable ambipolarity and Miller Capacitance and has a steeper subthreshold swing with improvement in ON state current compared to other conventional TFET structures. This work develops a physics based relevant analytical model of surface potential with the effect of gate fringing field and inclusive source and channel depletion region, drain current model, terminal charge and capacitance model to investigate its transient performance for the impact of ferroelectric polarization according to the Miller Ferroelectric polarization model. The proposed device is also validated using the SILVACO ATLAS device simulator, which yields a good agreement with the model, establishing its reliability and acceptability. The device achieves a steeper subthreshold of 31.3 mV/decade, an improved ON current∼5.9 x 10−4A/μmand enhanced transconductance∼0.105 ms as well as a very low energy delay product (EDP)∼4.07 x 10−3Js with hysteresis free operation at a low supply voltage∼0.5 V in a 40 nm technology node, making it desirable for ultra-low power analog and logic applications.

Scalable Synaptic Transistor Memory from Solution-Processed Carbon Nanotubes for High-Speed Neuromorphic Data Processing.

Neural networks as a core information processing technology in machine learning and artificial intelligence demand substantial computational resources to deal with the extensive multiply-accumulate operations. Neuromorphic computing is an emergent solution to address this problem, allowing the computation performed in memory arrays in parallel with high efficiencies conforming to the neural networks. Here, scalable synaptic transistor memories are developed from solution-sorted carbon nanotubes. The transistors exhibit a large switching ratio of over 105, a significant memory window of ≈12V arising from charge trapping, and low response delays down to tens of nanoseconds. These device characteristics endow highly stabilized reconfigurable conductance states, successful emulation of synaptic functions, and a high data processing speed. Importantly, the devices exhibit uniform characteristic metrics, e.g., with a 1.8% variation in the memory window, suggesting an industrial-scale manufacturing capability of the fabrication. Using the memories, a hardware convolution kernel is designed and parallel image processing is demonstrated at a speed of 1M bit per second per input channel. Given the efficacy of the convolution kernel, a promising prospect of the memories in implementing neuromorphic computing is envisaged. To explore the potential, large-scale convolution kernels are simulated and high-speed video processing is realized for autonomous driving.

An one-time pad cryptographic algorithm with Huffman Source Coding based energy aware sensor node design

Recently, the security-based algorithms for energy-constrained sensor nodes are being developed to consume less energy for computation as well as communication. For the mission critical wireless sensor network (WSN) applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. Therefore, in this manuscript, One-Time Pad Cryptographic Algorithm with Huffman Source Coding Based Energy Aware sensor node Design is proposed (EA-SND-OTPCA-HSC). Before transmission, the distance among transmitter and receiver is rated available in mission critical WSN for lessen communication energy consume of sensor node. For the mission critical WSN applications, continuous and secure data collection from WSN nodes is an essential task on the deployed field. The periodic sleep/wake up scheme with Huffman source coding algorithm is used to save energy at the node level. Then, one-time pad cryptographic algorithm in each sensor node, the vernam cipher encryption technique is applied to the compact payload. The proposed technique is executed and efficacy of proposed method is assessed using Payload Vs Energy consume for one sensor node, communication energy consume for one sensor node with different distances, energy consume for one sensor node under various methods, Throughput, delay and Jitter are analyzed. Then the proposed method provides 90.12 %, 89.78 % and 91.78 % lower delay and 88.25 %, 95.34 % and 94.12 % lesser energy consumption comparing to the existing EA-SND-Hyb-MG-CUF, EA-SND-PVEH and EA-SND-PIA techniques respectively.

Based on Deep Learning Model and Flink Streaming Computing Short Term Photovoltaic Power Generation Prediction for Suburban Distribution Network

With the advancement of global energy internet construction, accurate prediction of new energy generation power such as photovoltaic is an important foundation for ensuring the safety and economic working of new power systems. A short-term photovoltaic power generation prediction method for suburban distribution networks based on deep learning model fusion and Flink flow calculation is proposed to address the challenges of complex power grids, diversified disturbance factors, and isolated monitoring points. This method uses Bi directional Long Short Term Memory(BiLSTM) to extract cross sequential nonlinear characteristic of photovoltaic power generation time series data. Compared with standard LSTM, BiLSTM can consider both historical and future information simultaneously, thus extracting richer extracted features from power generation time series data. This method also integrates attention mechanism to capture the importance distribution of historical temporal features for power generation prediction, effectively solving the problem of long-term temporal dependence in standard LSTM models. The Flink streaming computing framework embeds a trained BiLSTM-Attention photovoltaic power generation prediction model, enabling real-time prediction and monitoring analysis of photovoltaic power generation at various monitoring points in the suburban distribution network. This article uses a dataset of a suburban photovoltaic power station for validation, and trains the model with historical power generation data, meteorological factors, weather types, seasons, and other information as inputs. The BiLSTM-Attention fusion model studys the temporal characteristics of power generation, and has high accuracy in predicting short-term photovoltaic power generation in different scenarios. The Flink streaming computing platform can not only process high throughput predicted power data, but also has low time delay.

Efficient and secure service function chain deployment method for delay optimization in air traffic information network

The air traffic information network is fundamental for ensuring the efficient and safe operation of flights. However, with the increasing demands of air traffic services, the rigidity of the traditional air traffic information network has become increasingly pronounced. Network function virtualization (NFV) technology presents an effective solution to address this issue. Within the NFV environment, the deployment of service function chains (SFC) forms the basis for achieving end-to-end air traffic services. This paper addresses the low delay and high security requirements inherent in air traffic services and proposes an efficient and secure SFC deployment for delay optimization (ESSFCD-DO) method. Primarily, the paper conducts a theoretical analysis of SFC deployment, and derives four key principles related to SFC delay, security, and resource cost. Subsequently, based on these principles, the ESSFCD-DO method is developed, which encompasses the virtual network function (VNF) aggregation algorithm for security and resources cost optimization (VNFAA-SRCO), the VNF deployment algorithm based on topology aware (VNFD-TA), and the algorithm for deploying virtual links based on k-shortest path. Experimental results demonstrate that, in comparison to other SFC deployment methods within this domain, ESSFCD-DO achieves significant optimizations in terms of end-to-end delay of SFC and long-term revenue-cost ratio, while ensuring security and service request acceptance rate.

Design of Routing Algorithm for Communication of Power Wireless Sensor Networks Based on Improved Harmony Search

In this paper, a routing algorithm for the communication of power wireless sensor networks based on improved harmony search is proposed to reduce the communication routing delay and energy consumption of power wireless sensor networks. Firstly, according to the composition of communication routing delay and energy consumption of power wireless sensor networks, the delay model and energy consumption model are constructed respectively, and the objective function is constructed by assigning different weights. Then, artificial bee colony algorithm and Levy flight mechanism improved harmony search algorithm are adopted for solving the objective function. Experiment results reveal that the algorithm can meet different users’ different demands on delay and energy consumption. If the user needs low delay, set the low delay weight to 1 and the power consumption weight to 0, then the average delay is 7s, and the average power consumption is 3203J. When user demand is low energy consumption, set the delay weight to 0 and the energy consumption weight to 1, so that the average energy consumption is 2667J and the average delay is 15s, which can meet the needs of users.

Efficient Schemes for Optimizing Load Balancing and Communication Cost in Edge Computing Networks

Edge computing architectures promise increased quality of service with low communication delays by bringing cloud services closer to the end-users, at the distributed edge servers of the network edge. Hosting server capabilities at access nodes, thereby yielding edge service nodes, offers service proximity to users and provides QoS guarantees. However, the placement of edge servers should match the level of demand for computing resources and the location of user load. Thus, it is necessary to devise schemes that select the most appropriate access nodes to host computing services and associate every remaining access node with the most proper service node to ensure optimal service delivery. In this paper, we formulate this problem as an optimization problem with a bi-objective function that aims at both communication cost minimization and load balance optimization. We propose schemes that tackle this problem and compare their performance against previously proposed heuristics that have been also adapted to target both optimization goals. We study how these algorithms behave in lattice and random grid network topologies with uniform and non-uniform workloads. The results validate the efficiency of our proposed schemes in addition to the significantly lower execution times compared to the other heuristics.

Low power process tolerant nano cache memory for military applications

ABSTRACT Rapid growth in high-performance battery-powered computing applications, Internet of Things (IoT) and artificial intelligence (AI), has raised the need for low-power integrated circuits. Particularly in military applications, wireless sensor nodes are used to detect border intrusion, weapon attacks, biometric wearables, internet of battlefield things (IoBT), etc. The computing and security devices utilised by the soldiers are powered by batteries. To increase the life and performance of these devices, the integrated circuits (IC) used within these devices need careful design. Particularly, an appropriate memory needs more attention because the design of memory has a substantial impact on IC performance as it occupies most of the chip space. In this article, a novel 8T transistor low-power cache memory cell is proposed and simulations for nominal chirality and dual chirality are done using HSPICE-compatible CNFET (carbon nanotube field effect transistor) technology. The power consumption is minimized in dual chirality case than nominal chirality. But still the power saving percentage of the proposed cache memory cell remains good compared to conventional memory structures. Thus the proposed cache memory cell provides low power, delay and energy apart from being process tolerant, thus proving that it is compatible for warfare and military applications.

An Efficient Time‐Sensitive Networking Traffic Scheduling Method for Train Communication Network

As a critical system of rail train, the train communication network (TCN) is an integrated central platform that is used to realize the train operation control, condition monitoring, and data transmission. With the development of train intelligence, the scale of data flow in TCN continues to expand, and the control data and monitoring data must be real‐time transmitted to ensure the safe and reliable operation of the train. Time‐sensitive network (TSN) can be applied as the next‐generation TCN solution, which provides end‐to‐end data transmission with extremely low delay and high reliability based on Ethernet. For the application of TSN technology in TCN, a single frame simple scheduling method is proposed to effectively reduce the worst end‐to‐end delay of the entire network, which improve the real‐time performance of TSN‐based TCN. The network topology and data flow characteristics in TCN are analyzed, a series of scheduling constraints are constructed, and the satisfiability modulo theories (SMT) is used to obtain a gate control list (GCL) to ensure the real‐time performance of time‐trigger streams. Finally, the TCN simulation model is established using OMnet++ software, the experiment results show that the proposed method can effectively reduce the overall delay and jitter, and meet the real‐time requirement of TCN. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Pre-hospital delay intention and its associated factors in the high-risk population of stroke: A latent profile analysis.

To explore the possible latent classifications of pre-hospital delayed intention at high risk of stroke and to analyze the characteristics of different profiles. A cross-sectional study was conducted in one community in Shanghai, China. 470 individuals at high risk of stroke were recruited, and self-reported questionnaires (including socio-demographic, stroke knowledge, health belief, and pre-hospital delay behavior intention scale) were distributed between April and June 2023. A latent profile analysis was employed to identify the delay intention clusters, and multinomial logistic regression was utilized to ascertain the factors influencing the latent classes of delay intention. 457 high-risk populations with a response rate of 97.23% were finally enrolled in this study. Four distinct classes were identified: High warning signs-Low delay intention (26.3%), Low warning signs-Low delay intention (17.7%), moderate level of delay intention (37.3%), and high level of delay intention (18.7%). The influencing factors included stroke knowledge, health belief, age, education background, the nearest distance to the medical institution, and household income. The pre-hospital delay intention among high-risk populations of stroke was classified into four distinct classes. It is crucial for individuals at high risk to remain vigilant towards stroke symptoms and to take prompt action. Health promotion education may be explored as a strategy to bridge the gap between the recognition of stroke symptoms and the low pre-hospital delay intention.

Applying Principal Component Analysis and Autoencoders for Dimensionality Reduction in Data Stream

This research focuses on the role and efficiency of Principal Component Analysis (PCA) and autoencoders, when working separately and concurrently for dimensionality reduction in large scale data. This is because the data obtained from sources such as the IoT sensors and the social media platforms is a lot more complicated than before and therefore proper dimensionality reduction is critical for real time analysis. The performance of these methods is analyzed on synthetic and real datasets based on which explained variance, reconstruction error and processing time of these methods are compared to define the optimal configuration. The results show that solely PCA is fast in linear data and autoencoders capture nonlinear dependence with slightly higher time complexity. This preserves considerable variance alongside a reasonable reconstruction error and thus makes the PCA-autoencoder model well suited to dynamic environments while incurring less computational expense than alternative PCA models. This work shows that it is possible to utilize relevant combinations of methods for dimensionality reduction to boost real-time data stream analysis especially in applications that demand for high accuracy at the same time as low delays.

Research on Differential Protection of Distribution Network Based on 5G Communication Technology

Abstract The application of traditional optical fiber in differential protection technology in distribution networks faces many challenges. Due to the physical characteristics and transmission limitations of optical fiber, there is a blind area for line fault isolation, which cannot meet the growing demand for access, and is not conducive to the popularization and application of differential protection in distribution networks. This paper proposes a differential protection method for distribution networks based on 5G communication technology. 5G communication technology has the characteristics of high bandwidth, low delay, high precision network timing and safety and reliability, and provides a new wireless communication method for differential protection of distribution network. The introduction of 5G communication technology can not only provide a more stable and fast data transmission channel to ensure that the differential protection system can respond quickly after receiving the fault signal, but also realize the precise monitoring of each node of the distribution network. At the same time, this paper combines network slicing technology, uplink scheduling optimization, bit error rate optimization, air retransmission optimization, transmission priority optimization and other methods to reduce the communication delay and delay jitter, improve the reliability of the method.

Dielectric Material and Thermal Optimization in Sidewall Spacer Design for Junctionless Nanosheet FETs at Sub- 5 nm Technology Node: An Insight into Device and Circuit Performance

This study focuses on the design and analysis of Junctionless (JL) NSFETs, with an emphasis on the influence of spacer materials and temperature variations. A different number of materials such as Air, SiO2, Si3N4, HfO2, and TiO2 are examined for sidewall spacer compatibility in the JL-NSFET. The same materials are used for dual material spacers with combinations of: Air+HfO2, Air+TiO2, SiO2+HfO2, and SiO2+TiO2. The investigations revealed that the usage of TiO2 material gives better digital and analog performance with reduced leakage currents and subthreshold swing (SS), higher on/off ratio, voltage gain of ∼79.7 dB. Exploring the dual-k spacers produced better analog performance, gate control and reduced leakages for SiO2+TiO2 owing to the usage of higher dielectric material towards the gate. Further, the reduction of temperature from 400 K to 250 K for all the single-k and dual-k spacer materials revealed that the designed JL-NSFET is a suitable candidate at lower temperatures to improve the digital and analog performance whereas not recommended for RF performance improvement. Moreover, the SiO2+HfO2 spacer-based CMOS inverter is noticed to have better gain (∼15 V/V), noise margin, and lower delays (∼5.1 ps) when compared to TiO2 spacer-based complementary metal oxide semiconductor inverter making it suitable for digital IC applications.

An Adaptive Strong Tracking Volume Kalman Filter Algorithm

Abstract Based on dental implant technology, this paper studies the position and posture tracking of surgical tools during the treatment of patients. A strong tracking adaptive volume Kalman filter (STF-ACKF) algorithm is proposed to meet the requirements of high accuracy, low time delay and strong robustness due to the complexity of the oral space and the narrow motion environment, to improve the measurement accuracy and reduce the noise interference. The adaptive module is added to the algorithm, and the real-time correction of process noise variance is realized by using MAP estimator [1-3]. The variance of the measured noise [4-5] was estimated using the variational Bayes (VB) method. Finally, the theoretical analysis and simulation results show that the mean error of STF-ACKF on X-axis, Y-axis and yaw angle is reduced by 1.17 mm, 0.76 mm and 0.62 ° respectively, the estimation accuracy and stability are improved significantly.

Design of voice command recognition chip based on heterogeneous acceleration

Abstract Speech, as one of the earliest forms of communication used by humans, can effectively convey information. However, the current deep neural network models for speech recognition are generally large in scale and can only be deployed in the cloud, which imposes high deployment environment requirements and power consumption, thereby limiting their implementation on embedded devices. In the context of end-to-end speech recognition, a series of challenges are encountered, including power consumption constraints, computing power limitations, network dependencies, privacy protection, bandwidth restrictions, and communication delays. To address these issues, this paper proposes the design of an end-to-end voice command recognition chip based on deep neural networks specifically for recognizing voice commands in specific scenarios. This chip achieves low power consumption and minimal delay in recognition. Additionally, we introduce a weighted, overloadable chip architecture to enable seamless scene migration, ultimately aiming to resolve the aforementioned challenges.