Parallel Data Transmission Research Articles

HF-NVIS Data Communication: OFDM Transmission Testing and Optimization Strategies

Abstract Orthogonal frequency division multiplexing (OFDM), a technique that uses frequency division multiplexing to allow parallel transmission of data over multiple subchannels, is widely used in various wireless systems. Its effectiveness even extends to HF links, where it has demonstrated superior performance compared to single-carrier schemes. In addition, OFDM’s robustness to multipath fading and its ability to efficiently utilize the available bandwidth make it a preferred choice for modern communication systems, but also for scenarios with complex communication links, such as HF range communications. The objective of this experimental research is to analyze and improve the performance of the OFDM technique adapted to HF communication channels. This study is motivated by the lack of a protocol to regulate HF communications using OFDM. The analysis and optimization method starts by considering the fundamental bandwidths of HF channels (ranging from 3 to 24 kHz). For this purpose, OFDM-specific modifications have been analyzed, including changes in the FFT size (representing the number of subcarriers), adjustment of the number and amplitude of pilots, analysis of the impact of packet length modification (pre-OFDM), and evaluation of guard intervals. In essence, our goal is to implement an OFDM technique commonly used in modern data communications in the context of traditional HF communications systems.

Design of a single laser source-based extreme parallel data-transmission link

Design of a single laser source-based extreme parallel data-transmission link

PaCHNOC: Packet and Circuit Hybrid Switching NoC for Real-Time Parallel Stream Signal Processing.

Real-time heterogeneous parallel embedded digital signal processor (DSP) systems process multiple data streams in parallel in a stringent time interval. This type of system on chip (SoC) requires the network on chip (NoC) to establish multiple symbiotic parallel data transmission paths with ultra-low transmission latency in real time. Our early NoC research PCCNOC meets this need. The PCCNOC uses packet routing to establish and lock a transmission circuit, so that PCCNOC is perfectly suitable for ultra-low latency and high-bandwidth transmission of long data packets. However, a parallel multi-data stream DSP system also needs to transmit roughly the same number of short data packets for job configuration and job execution status reports. While transferring short data packets, the link establishment routing delay of short data packets becomes relatively obvious. Our further research, thus, introduced PaCHNOC, a hybrid NoC in which long data packets are transmitted through a circuit established and locked by routing, and short data packets are attached to the routing packet and the transmission is completed during the routing process, thus avoiding the PCCNOC setup delay. Simulation shows that PaCHNOC performs well in supporting real-time heterogeneous parallel embedded DSP systems and achieves overall latency reduction 65% compared with related works. Finally, we used PaCHNOC in the baseband subsystem of a real 5G base station, which proved that our research is the best NoC for baseband subsystem of 5G base stations, which reduce 31% comprehensive latency in comparison to related works.

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

Efficient and Fair: Information-Agnostic Online Coflow Scheduling by Combining Limited Multiplexing With DRL

In shared data center networks, communications among users can be modeled as coflows, each comprising of a group of parallel data transmission flows. Efficient and fair scheduling of coflows is critical for improving both system performance and user satisfaction at the application level. Existing coflow scheduling methods maximizing efficiency (coflow completion time, CCT) and fairness (service isolation) simultaneously require prior knowledge of coflow (flow) size that is however not known before completion of coflow execution in reality, which limits their applicability. For information-agnostic scheduling, known results focus either solely on efficiency or fairness, but not both due to the hardness of achieving the desired compromise between them. In this paper, we first present an information-aware non-preemptive coflow scheduling algorithm, and show its provable long-term isolation guarantee under reasonable assumptions. We then adapt this algorithm to information-agnostic online coflow scheduling by combining limited multiplexing with Deep Reinforcement Learning (DRL) framework to achieve long-term isolation guarantee toward fair network sharing and lower average weighted CCT simultaneously. The simulation results show that our algorithm outperforms the state-of-the-art results of both fairness-optimal scheduling (NC-DRF) by 4.92in terms of average weighted CCT and performance-optimal scheduling (Aalo) in the metric of maximum normalized CCT. This fully demonstrates the superiority of our method in simultaneous optimization of efficiency and fairness for information-agnostic coflow scheduling.

Mathematical modeling method based on heterogeneous cellular network algorithm and computer multi-dimensional space

Due to the rapid development of services such as computer multi-dimensional space, traditional cellular networks are quantified and diversified, and it is difficult to meet the future connection needs of green network users. The heterogeneous cellular network algorithm places multiple microcells and relay nodes under the macrocell. This multi-layer network architecture can not only shorten the data transmission link, but also reduce the energy consumption of base stations and terminals. In addition, through spectrum space reuse Capability and spectrum efficiency improve the efficiency of the system, but this also increases the number of base stations and network construction costs, and significantly increases energy consumption and operating costs of operators' electricity expenditures. In addition, in order to achieve parallel transmission of data, placing multiple millimeter wave input and output antenna systems in heterogeneous network microcells can meet the high-speed data needs of indoor users, but the antenna array requires many RF links to drive, which increases the hardware implementation. Complexity, cost and energy consumption. According to the above-mentioned problems, a reconfigurable modular robot is established based on the multi-dimensional computer space. It is composed of modules with the same interface and can be assembled into different configurations according to different tasks. Compared with the original robot, the reconfigurable modular robot is more adaptable to the work and environment, and flexible. Then the basic and core technologies such as kinematics, type synthesis, scale synthesis, error and control of the reconfigurable modular robot are mathematically modeled, so as to effectively apply and promote the reconfigurable modular robot in practice, and make it has theoretical and practical value.

High speed 60 Gbps RGB laser based-FSOC link by incorporating hybrid PDM-MIMO scheme for indoor applications

Abstract Visible Light Communication (VLC) systems enhanced by red, green and blue (RGB) lasers are at the forefront of indoor technology, offering dynamic lighting, high-speed data transfer, and energy efficiency. This innovative combination not only revolutionizes connectivity and illumination but also ensures privacy and security, making it a game-changer for smart homes, offices, and various indoor applications. In our research, we introduce a polarization division multiplexing and Multiple Input Multiple Output based (PDM-MIMO) system that carries 60 Gbps of data over a transmission range of 500 m in free space Channels. The utilization of the cost-effective on-off key (OOK) modulation format is attributed to its affordability in our transmission scheme. For parallel data transmission, three laser diodes in RGB were utilized. To enhance both the transmission range and reduce the Bit Error Rate (BER), MIMO scheme is employed. Our study presents simulation outcomes, conducted using OptiSystemTM software, that focus on evaluating the bit error rates for the proposed PDM-MIMO link. Our findings demonstrate successful 60 Gbps data transmission over 350 m in FSO with an acceptable BER, reinforced by clear eye diagrams. Introducing MIMO expands the range to 500 m while improving BER, paving the way for real-time experimentation and research advancement.

Massively scalable Kerr comb-driven silicon photonic link

The growth of computing needs for artificial intelligence and machine learning is critically challenging data communications in today’s data-centre systems. Data movement, dominated by energy costs and limited ‘chip-escape’ bandwidth densities, is perhaps the singular factor determining the scalability of future systems. Using light to send information between compute nodes in such systems can dramatically increase the available bandwidth while simultaneously decreasing energy consumption. Through wavelength-division multiplexing with chip-based microresonator Kerr frequency combs, independent information channels can be encoded onto many distinct colours of light in the same optical fibre for massively parallel data transmission with low energy. Although previous high-bandwidth demonstrations have relied on benchtop equipment for filtering and modulating Kerr comb wavelength channels, data-centre interconnects require a compact on-chip form factor for these operations. Here we demonstrate a massively scalable chip-based silicon photonic data link using a Kerr comb source enabled by a new link architecture and experimentally show aggregate single-fibre data transmission of 512 Gb s−1 across 32 independent wavelength channels. The demonstrated architecture is fundamentally scalable to hundreds of wavelength channels, enabling massively parallel terabit-scale optical interconnects for future green hyperscale data centres.

Scaling comb-driven resonator-based DWDM silicon photonic links to multi-Tb/s in the multi-FSR regime

The use of chip-based micro-resonator Kerr frequency combs in conjunction with dense wavelength-division multiplexing (DWDM) enables massively parallel intensity-modulated direct-detection data transmission with low energy consumption. Resonator-based modulators and filters used in such systems can limit the number of usable wavelength channels due to practical constraints on the maximum achievable free spectral range (FSR). In this work, we introduce the design of multi-Tb/s comb-driven resonator-based silicon photonic links by leveraging the multi-FSR regime. We demonstrate the viability of the link architecture with yield estimates that are supported by extensive wafer-scale measurements of 704 micro-resonators fabricated in a commercial complementary metal–oxide–semiconductor foundry. We show that a 2.80 Tb/s link is realizable with a ≥6σ yield (∼99.999%), and that aggregate bandwidths of 3.76 Tb/s and 4.72 Tb/s are possible if yield targets are relaxed (3σ and 1σ, respectively). All designs represent a 1.94−3.28× boost to aggregate link bandwidth while maintaining BER≤10−10 performance, with a theoretical bandwidth of 10.51 Tb/s being possible for sufficiently robust resonators. We use high-speed BER measurements to inform co-optimization of data rate and aggressor spacing (λag), limiting any additional loss-based power penalties to off-resonance insertion loss (IL) and routing loss. This work demonstrates that, through the multi-FSR regime, there is a clear path toward Kerr comb-driven ultra-broadband, high bandwidth silicon photonic links that can support next-generation data centers and high-performance computers.

Online scheduling of coflows by attention-empowered scalable deep reinforcement learning

With the abstraction of parallel data transmission flows being a coflow, data transmissions in large-scale computing jobs can be modeled by a coflow directed acyclic graph (coflow DAG) in which nodes are coflows and edges represent dependencies between coflows. Efficient scheduling of coflows on network links is crucial for reducing the overall communication and job completion time. The known best coflow scheduling method deploying deep reinforcement learning (DRL), DeepWeave (Sun et al., 2020), suffers from poor scalability due to the requirement of O(dn)-size policy network for processing n coflows of d dimensions which is difficult to train. This paper extends the directed acyclic graph neural network (DAGNN) to Pipelined-DAGNN that embeds the features of different stages of input coflow DAGs in pipeline to effectively speed up the feature extraction process. To effectively process the feature vectors of coflow DAGs of arbitrary size and shape without compromising scheduling accuracy (quality), we propose a novel self-attention empowered DRL coflow scheduling model to generate coflow scheduling policies, which enables the scale of policy network depends only on features (dimensions) rather than coflows, without the need of packing all individual embedding vectors from Pipelined-DAGNN into a long flat vector. Our model reduces the size of the policy network in DRL from previously O(dn) to O(d), achieving a high scalability independent of the number of coflows. Simulation results on Facebook trace show that our model reduces the average weighted job completion time by up to 33.88%, apart from being more scalable and robust, compared with the state-of-the-art methods.

Quantum Dot Light‐Emitting Synaptic Transistor for Parallel Data Transmission of Diverse Artificial Neural Network

AbstractArtificial synaptic devices serve as the cornerstone of artificial neural networks, much research is devoted to the development of artificial synaptic devices with multiple functions for the future construction of large‐scale artificial neural networks. By adding optical signal output to traditional synaptic devices, the strategy of transforming the devices from a single electrical interconnection to an optoelectronic interconnection is considered to be an effective way to solve the problem of wire cross‐talk in large‐scale artificial neural networks. Herein, a quantum‐dot light‐emitting synaptic transistor capable of dual output of optoelectronic signals by integrating the functions of light‐emitting transistor and synaptic transistor into a single device is demonstrated for the first time. Based on the novel working mechanism and the excellent optoelectronic properties of quantum dots, the device can exhibit dual responses of electrical and optical signals under electrical pulse stimulation. More importantly, some key synaptic functions such as excitatory postsynaptic current, paired pulse facilitation, high‐pass filtering properties, and the transition from short‐term memory to long‐term memory are successfully simulated in the device. In addition, classical conditioned reflex experiments as well as the processes of learning and forgetting are optically and electrically simulated. This work provides a feasible way to realize multivariate artificial neural networks with high integration and optoelectronic interconnection to transmit information, showing great potential in the development of neuromorphic computing in the future.

A Simultaneous Power and Data Transmission Technology Based on Coil Multiplexing in Domino-Resonator WPT Systems

To achieve parallel power and data transmission in domino-resonator wireless power transfer (WPT) systems, a novel simultaneous power, and data transfer (SPDT) technology based on coil multiplexing is proposed in this letter. Benefiting from the magnetic field characteristics of the bipolar coil structure, the power and data can be injected into multiplexed coils with different flow directions, leading to opposite induced voltage polarities in each coupling coil. Subsequently, a corresponding resonator circuit is developed to form independent power and data resonance loops. In addition, an injected data transmission scheme is developed for domino-resonator WPT systems. Compared with traditional SPDT methods, additional wave trappers and coupling structures are avoided by this proposed technology, and the interference between power and data transfer is significantly eliminated. A 23.6 W laboratory prototype with five domino resonators is built to validate the feasibility of this proposed technology. The experimental results show that the power transfer efficiency reaches 70% at 50 kb/s data transfer rate.

Reducing the impact of interchannel interference on the efficiency of signal transmission in telecommunication systems of data transmission based on the ofdm signal

This paper considers the process that forms an interchannel transient interference in the structure of the signal created on the basis of the technology of parallel data transmission and frequency distribution with multiplexing of the phase-modulated signal – the OFDM signal. Based on the analysis of the OFDM signal structure, it was determined that changes in the position and parameters of the carrier symbol from the composition of this OFDM symbol create an interchannel transient interference. A list of OFDM signal parameters that can affect the appearance of interchannel interference and the value of its quantitative value was summarized and presented. A model for assessing the impact of interchannel interference on the efficiency of signal transmission in telecommunication data transmission systems based on the OFDM signal has been developed and proposed. Based on mathematical modeling using this model, the dependence of the quantitative assessment of the magnitude of the interchannel interference on the magnitude of the protective interval for different values of the interchannel value with a different number of signal pre-reception has been established. It is shown that an increase in the value of the interchannel value to 96 subchannels makes it possible to achieve an interchannel transient interference of less than 3 percent with a protective interval of more than 2 ms already with one pre-reception. This is explained by the fact that the increase in the interchannel value makes it possible to reduce the value of the protective interval and minimizes the effect of frequency distortions of the sub-channel of one channel. The data reported in this work and the recommendations substantiated on their basis confirm the possibility of the proposed model for assessing the value of the interchannel transient interference and justifying the recommendations for reducing its impact on the efficiency of signal transmission in telecommunication data transmission systems based on the OFDM signal. The proposed evaluation model can find practical application in improving existing and developing new telecommunication data transmission systems based on OFDM technology

Load Balancing With Deadline-Driven Parallel Data Transmission in Data Center Networks

With the explosive growth of the Internet of Things (IoT), an increasing amount of sensor data generated by soft real-time IoT applications has been moved to data centers for storage and data analysis. Large amounts of these data are required to be processed within a given deadline to ensure application performance. Therefore, meeting the transmission deadlines of data flows for soft real-time applications has always been crucial yet challenging to current data centers. Recent progress has demonstrated that adopting parallel data transmission over multipath data center network combining with effective load balancing can achieve a high bisection network bandwidth, thus speeding up the network transfer of data flows. Nevertheless, the deadline miss ratios (DMRs) of these flows are not lowered as expected since the existing load balancing schemes are naturally agnostic to the deadline requirement. They are either unable to reroute traffic flexibly or aimlessly reroute these deadline-restrained flows, regardless of their urgent levels and path conditions. To address these inefficiencies, we propose a deadline-aware load-balancing scheme, namely, DLB, which perceives the deadline requirements and helps the urgent flows to timely switch to those faster transmission paths to complete quickly. Specifically, DLB computes the urgent level for each flow in real time to judge if the switch needs to make proactive rerouting. When a flow is nonurgent, DLB does not proactively change its transmission path, leaving more available paths to those flows with higher urgent levels. When a flow becomes extremely urgent, it immediately switches to those light-loaded paths to finish its data transmission before its deadline as far as possible. Experimental results of NS2 simulations and real testbed implementations show that DLB reduces the DMRs by up to 50% compared to the state-of-the-art data center load-balancing schemes, while only induces trivial overhead during deployment.

Digital management of electricity consumption as a factor of transport company value

Transport industry enterprises consume a significant amount of electricity, and management impacts on this process with the help of digital technologies will reduce costs and allow to obtain other beneficial effects. The paper points out that the development of transport infrastructure, electric transport and all categories of smart transport increases the dependence of the efficiency of the functioning of transport companies on the consumed volumes and quality of electric energy. The authors considered the effects from using new methods of digital management of resource consumption, which include increasing the stability of the power system, increasing the accuracy of measuring consumed resources, as well as solving the problems of effective planning and resource allocation. The influence of the effects of digital management of electricity consumption is considered from the standpoint of value-based management. The key conditions for the information support of the functioning of control systems is a change in the format and method of information exchange, i.e. the transition from the transmission of analog data to digitized information blocks. The results of the study are to confirm the positive impact of the use of digital technologies in the management of electricity consumption, primarily on reducing operating costs and, as a result, increasing the value of transport companies, as well as highlighting the prospects for their widespread use in transport systems, which may be the subject of future research.

OFDM Spectrum Sensing Analysis and Implementation Method

OFDM is a carrier modulation technology, which realizes the parallel transmission of high-speed serial data through frequency division multiplexing. OFDM has an excellent ability to resist multipath fading and is commonly used in wireless communication transmission technology. Spectrum sensing technology is a technology that allows cognitive users to accurately obtain the authorized spectrum usage in wireless networks through signal detection and other processing methods. It is the basis for effectively using different idle channels in different time domains. This paper mainly discusses the OFDM technology, deduces the algorithm of the implementation principle and mathematical characteristics of OFDM, analyses its spectrum characteristics and performance advantages, focuses on the principle and method of OFDM spectrum sensing based on spectrum sensing technology, combs the characteristics of several existing commonly used OFDM spectrum sensing methods, and analyses the implementation cost, hardware requirements, transmission performance The performances of interference factors and other aspects are sorted out, the advantages and disadvantages of several existing OFDM spectrum sensing methods are compared, their respective applicable environments are compared, and their possible problems in the future development prospect of OFDM technology and the direction that needs further research and improvement are put forward.

Analysis, Design, and Experimental Verification of a Parallel Wireless Power and Data Transmission Method for Rotary Steering Systems

In the rotary steering system of oil drilling, both power and data transmission are needed. This paper presents a parallel power and data transmission method for a rotating steering system with output voltage control. To reduce the size of the system, power and data are transmitted through the same rotational coupling mechanism. A power transfer resonance circuit is used to suppress the influence of the power transfer on data transmission. Therefore, crosstalk interference between the power and the data transmission channel is negligible. The experimental prototype is built, and the feasibility of the data transfer method and the closed-loop control method is verified. The experimental results are in good agreement with the theoretical analysis.

Environmental Accounting System Model Based on Artificial Intelligence Blockchain and Embedded Sensors.

With the rapid development of communication technology and automation technology, sensors are becoming more and more intelligent. This study proposes an environmental accounting system model based on artificial intelligence blockchain and embedded sensors. A high-precision sensor system based on embedded technology is first built, which not only avoids the shortcomings of analog data transmission, but also has high performance and price advantages. At the same time, it can be easily combined into a simple sensor network, and array measurement can be realized through the development of blockchain technology. However, the current blockchain system has the disadvantage of storage limitation. The problem is particularly serious when a large amount of data needs to be stored in the blockchain, especially when the blockchain is combined with big data. As a comprehensive field of accounting, ecology, and environmental science, environmental accounting has made great contributions to sustainable development in the field of accounting. The theoretical research in the field of environmental accounting in China started late, and a unified environmental accounting system has not actually been established. Under the conditions of immature theoretical and policy basis, companies have almost no actual surveys on environmental accounting. The characteristics of blockchain technology, such as decentralization, transparency, and changes in information unavailability, can fully solve the problem that the current accounting information system cannot consolidate transaction information and the accounting process. The reliability assurance mechanism of the accounting information system based on blockchain technology can greatly ensure the reliability of the accounting information system, effectively suppress accounting fraud, and improve the transparency of information.

Highly Energy-Efficient Metaconductor-Based Integrated RF Passives: Metaconductor-Based RF Passives

Human beings are naturally mobile and crave freedom. But, for many years, we were leashed to our office desks/homes to make a long-distance phone call. Finally, in 1973, the first-ever cellular phone call was made by Motorola engineer Dr. Martin Cooper using a handmade prototype device that weighed almost 2.5 lb. As Greek philosopher Heraclitus said, “Change is the only constant,” and over the past 50 years, cellular phone technology has undergone a drastic makeover where we drifted from a 2.5-lb cellular phone to a 0.4-lb smartphone (an iPhone 13), from typewriters to digital systems like desktops and laptops, and from paper maps to GPS for navigation. Similarly, as shown in <xref ref-type="fig" rid="fig1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Figure 1</xref> , our journey in wireless mobile communications started with 1G with analog data transmission and a data rate at ∼2.4 Kb/s to a full-fledged and operating 4G at a maximum data rate of 1 Gb/s.

Research on Enterprise Financial Accounting Information Security Model Based on Big Data

Powered by the rapid development of the information age, big data technology has had a great impact on China’s socialist economic development. Big data technology is integrated into all fields of nowadays society, especially in the accounting industry of enterprises. Accounting computerization quickly replaces the traditional manual bookkeeping, realizes the timeliness and accuracy of accounting information data processing, and improves the work efficiency and quality of accounting staff to a great extent, but at the same time, the security of financial accounting information is also very prominent. To tackle this problem, this paper proposes a hybrid encryption algorithm based on double chaotic system and improved AES encryption algorithm. The improved AES algorithm uses affine transformation pairs (A7 and 6F) to generate new S-boxes. The double four-dimensional hyperchaotic system is transformed from two three-dimensional chaotic systems, and then, the transformed hyperchaotic system is used to generate chaotic sequences, and a block encryption scheme is designed. On Hadoop big data platform, double hyperchaotic encryption scheme and improved AES algorithm are combined. Simulation test results show that this method can safely transmit enterprise financial accounting information data packets. With the increase of computing cluster nodes, its encryption transmission efficiency continues to improve. This scheme not only solves the problem of enterprise financial accounting data security encryption but also realizes the parallel transmission of encrypted data in the information age, forming a double guarantee of data transmission security and efficiency.