Improve Transmission Performance Research Articles

Sampled-data event-triggered control with sampling-independent positive guarantees on inter-event times

To fit with a digital environment in networked control systems, sampled-data event-triggered control (ETC) has been proposed where the event-triggering mechanisms sense and process information only at discrete sampling instants (not necessarily periodically). One deficiency in the previous study on sampled-data ETC is the lack of analysis on transmission performance, which yields the potential occurrence of an undesirable phenomenon that the minimum inter-event time is always equal to the minimum inter-sampling interval, no matter how small the latter is. To overcome this drawback, in this paper, we propose a novel sampled-data ETC scheme such that the lower bounds of inter-event times have sampling-independent positive guarantees. A linear networked control system is considered under observer-based controllers, external disturbances, and multiple communication channels. The improved transmission performance is due to the utilization of dynamic event-triggering conditions and some mean-rate error signals, which are the ratios between network-induced errors and some time-increasing functions. After modeling the closed-loop dynamics into a hybrid system, sufficient conditions on the upper bound of inter-sampling intervals and parameters in ETC are provided to ensure input-to-state stability (rather than its practical version) and sampling-independent positive guarantees simultaneously. Furthermore, a new tradeoff relationship between the sampling and transmission performance is revealed: a faster sampling frequency is conducive to improving inter-event times. Finally, a linearized model of an inverted pendulum is simulated to illustrate the efficiency and feasibility of the obtained results.

A robust parity‐time‐symmetric hybrid wireless power transfer system with extended coupling range

SummaryInductive power transmission (IPT) and capacitive power transmission (CPT) are currently the two main wireless power transfer technologies. Hybrid power transmission (HPT) systems that combine IPT and CPT can improve transmission performance and are more attractive for some charging applications such as electric bicycles (e‐bikes). However, HPT systems are more sensitive to parameter variations, and the coupling mechanism is prone to misalignment during wireless charging. Therefore, achieving stable output power and constant transfer efficiency over a wide range of coupling coefficient variations is still a major challenge for HPT systems. In this work, the parity–time (PT) symmetry theory is applied to an SS‐type HPT system, and a circuit model of the PT‐based HPT system is developed by using coupling capacitance and coupling inductance. The effect of connecting the plates to the homonymous end or heteronymous end of the coupling coils on the PT symmetry range of the HPT system is discussed. It is found that the heteronymous end connection of the coils can effectively increase the range of constant output power and stable transfer efficiency. Finally, a prototype is built to verify the feasibility and effectiveness of the proposed PT‐based HPT system. The transfer power and efficiency remain constant in the PT‐symmetric range, and the inductive and capacitive coupling coefficients are smaller compared to IPT and CPT systems with the same parameters.

Performance evaluation of the data transmission link with Bessel-Gaussian beams through a tunable smoke channel

We simulate and experimentally demonstrate a 1 Gbps free-space optical (FSO) communication system in a smoke chamber with the Bessel–Gaussian (BG) beam. Firstly, we simulate and analyze the attenuation and bit error rate (BER) of BG and Gaussian beams in the smoke channel based on the Monte Carlo method. The results show that BG beams have more advantages than Gaussian beams in smoke transmission. Then we generate a BG beam based on the spatial light modulator (SLM) method, the BG beam’s central spot shows a stable diameter of about 0.9 mm propagating 3.2 m in space. The 1 Gbps 1550 nm on/off keying (OOK) signal is loaded on the BG beam via a bit error rate test (BERT). Next, a tunable smoke channel is established based on an acrylic chamber, and the attenuation and visibility of the smoke channel are characterized in real-time. Finally, the intensity distributions, power stability, power attenuation and BER performance of the BG and Gaussian beams’ transmission links in the tunable smoke channel are investigated. The experimental results show the improvements in transmission performance under the smoke channel conditions using BG beams. Compared with Gaussian beams, BG beams exhibit lower power attenuation (reduced by up to 3.13 dB) and a more stable receiver (decreased by 23 % in standard deviation). Additionally, the BER of 1 Gbps of the transmission link with the BG beam improved by 7.18 dB–10.17 dB in the tunable smoke channel (0.5–2 m). As the length of the smoke channel increases, the improvement of the BG beam in the smoke channel increases as well. The BG beam has shown remarkable advantages in transmission links through the smoke channel. The research offers a robust and generalized proposal for FSO communications based on structured beams.

Energy-efficient access point clustering and power allocation in cell-free massive MIMO networks: a hierarchical deep reinforcement learning approach

Cell-free massive multiple-input multiple-output (CF-mMIMO) has attracted considerable attention due to its potential for delivering high data rates and energy efficiency (EE). In this paper, we investigate the resource allocation of downlink in CF-mMIMO systems. A hierarchical depth deterministic strategy gradient (H-DDPG) framework is proposed to jointly optimize the access point (AP) clustering and power allocation. The framework uses two-layer control networks operating on different timescales to enhance EE of downlinks in CF-mMIMO systems by cooperatively optimizing AP clustering and power allocation. In this framework, the high-level processing of system-level problems, namely AP clustering, enhances the wireless network configuration by utilizing DDPG on the large timescale while meeting the minimum spectral efficiency (SE) constraints for each user. The low layer solves the link-level sub-problem, that is, power allocation, and reduces interference between APs and improves transmission performance by utilizing DDPG on a small timescale while meeting the maximum transmit power constraint of each AP. Two corresponding DDPG agents are trained separately, allowing them to learn from the environment and gradually improve their policies to maximize the system EE. Numerical results validate the effectiveness of the proposed algorithm in term of its convergence speed, SE, and EE.

Chaotic phase noise-like encryption based on geometric shaping for coherent data center interconnections.

The network traffic of data centers (DCs) has increased unprecedentedly with the rapid development of digital economy. However, the data transmission faces security threats in the distributed optical interconnection and intensive interaction of DC networks. In this paper, we propose a chaotic phase noise-like encryption algorithm using geometric shaping (GS) for coherent DC interconnections (DCIs). A GS constellation is used to improve transmission performance, and it is combined with coherent equalization algorithms to improve security performance. Then, a chaotic encryption is designed based on phase noise-like transformation (PNLT). The data are effectively scrambled, and the confusion level of phase can be increased. Finally, 216 Gb/s 8-quadrature amplitude modulation (8-QAM) encrypted data are successfully verified on a 240 km transmission link of DCIs. The results show that this scheme can achieve a bit error rate (BER) performance gain of 1.1 dB and provide a highly compatible solution for realizing security enhanced DCIs.

A Mean-Rate Event-Triggered Mechanism for Nonlinear Plants With Weak Time Regularization

This paper proposes a new event-triggered mechanism for nonlinear plants with multiple communication networks. An auxiliary signal that contains a component to measure the (weighted) mean changing rates of measurement errors is employed; and hence the induced event-triggered mechanism is termed as a mean-rate one. By incorporating this auxiliary signal into dynamic event-triggering conditions, the new mean-rate event-triggered mechanism can ensure closed-loop input-to-state stability with respect to external disturbances. To further improve transmission performance, a concept of weak time regularization is given and proved, where similar to traditional time regularization, no events can be triggered within a user-specified lower bound; but differently, the positiveness of this user-specified parameter is not necessary in excluding Zeno behavior. Finally, a nonlinear example is simulated to illustrate the feasibility and efficiency of the theoretical results.

Reinforcement learning based multi-parameter joint optimization in dense multi-hop wireless networks

Carrier Sense Multiple Access with Collision Avoid (CSMA/CA) restricts the channel utilization efficiency although it always is regarded as a promising distributed channel access scheme, especially in dynamic dense wireless network scenarios. This paper investigates a Reinforcement Learning (RL) based multi-parameter joint optimization mechanism to build suitable access configuration for dense wireless multi-hop networks. Specifically, we propose a light-weight CSMA/CA mechanism based on Q-Learning (QL), namely QL-CSMA/CA, which jointly optimizes multiple channel access parameters, including Contention Window (CW), Maximum Backoff Exponent (MaxBE), and Maximum CSMA Backoffs (MaxCSMABackoffs), by setting mixed rewards and thus leading to higher network performance. Due to the limitation of the QL-CSMA/CA in large-scale state-space scenarios, we further present an enhanced CSMA/CA mechanism based on Deep Q-Network (DQN), namely DQN-CSMA/CA. Despite introducing a little complexity, the DQN-CSMA/CA can significantly improve transmission performance by utilizing more comprehensive network state information in complex dynamic network scenarios. Simulation results demonstrate the effectiveness of the proposed algorithms in terms of throughput, packet loss rate, delay, and collision rate, especially in high-density scenarios. Specifically, compared to SCAMA and traditional CSMA/CA algorithms, the QL-CSMA/CA algorithm achieves 9% and 5% performance improvements in terms of throughput, while DQN-CSMA/CA algorithm achieves 14% and 10% improvements in terms of throughput, respectively.

Energy-efficient power control strategy of the delay tolerable service based on the reinforcement learning

In recent years, the rapid development of Internet technology and its applications has led to an exponential growth in the number of Internet users and wireless terminal devices, resulting in a corresponding increase in energy consumption. This has necessitated the need to reduce energy consumption while maintaining the quality of communication services. To this end, we investigate the possibility of improving energy efficiency (EE) of delay tolerable (DT) services by allocating resources based on the time-domain water-filling algorithm. We first transform the non-convex problem of maximizing EE into a convex problem of minimizing transmission power to obtain the optimal solution, and then use the greedy algorithm to obtain an upper bound. Furthermore, to capture a more realistic scenario, an Approximate Statistical Dynamic Programming (ASDP) algorithm is introduced, but its effect on enhancing EE is limited. To overcome this limitation, three Deep Reinforcement Learning (DRL) algorithms are implemented. The simulations results show that the settings of maximum transmit power and SNR during agent training have an impact on the performance of the agent. Finally, by comparing the mean values of transmission power, outage probability, equilibrium power and performance improvement percentage of several algorithms, we conclude that the Deep Deterministic Policy Gradient (DDPG) algorithm produces the best agent performance in the environment with a fixed SNR of 2 (dB).

Current sharing optimization of multilayer high temperature superconducting cable

Large-capacity high-temperature superconducting (HTS) cables generally adopt a multilayer structure to increase the current-carrying capacity. However, the multilayer structure of the HTS cables will increase the uneven current distribution, decreasing the critical current and heighten the AC loss. Therefore, the current sharing technology between each layer of the HTS cable is essential to improve transmission performance. This paper proposes two hybrid HTS current sharing optimization algorithms based on particle swarm optimization and artificial bee colonies. The current distribution of the HTS cable in each layer under pre- and post-optimization is estimated and the structure parameters are obtained. A 3D model is established to evaluate the characteristics of the optimized HTS cable. The results show that the proposed hybrid current sharing optimization schemes can well improve the inner current sharing of the HTS cable, and the feasibility and effectiveness are therefore demonstrated.

Performance Improvement of Multi-Robot Data Transmission in Aggregated Robot Processing Architecture with Caches and QoS Balancing Optimization

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish–subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.

Optical amplification-free deep reservoir computing-assisted high-baudrate short-reach communication.

An optical amplification-free deep reservoir computing (RC)-assisted high-baudrate intensity modulation direct detection (IM/DD) system is experimentally demonstrated using a 100G externally modulated laser operated in C-band. We transmit 112 Gbaud 4-level pulse amplitude modulation (PAM4) and 100 Gbaud 6-level PAM (PAM6) signals over a 200-m single-mode fiber (SMF) link without any optical amplification. The decision feedback equalizer (DFE), shallow RC, and deep RC are adopted in the IM/DD system to mitigate impairment and improve transmission performance. Both PAM transmissions over a 200-m SMF with bit error rate (BER) performance below 6.25% overhead hard-decision forward error correction (HD-FEC) threshold are achieved. In addition, the BER of the PAM4 signal is below the KP4-FEC limit after 200-m SMF transmission enabled by the RC schemes. Thanks to the use of a multiple-layer structure, the number of weights in deep RC has been reduced by approximately 50% compared with the shallow RC, whereas the performance is comparable. We believe that the optical amplification-free deep RC-assisted high-baudrate link has a promising application in intra-data center communications.

Multi-Degree-of-Freedom for Underwater Optical Wireless Communication with Improved Transmission Performance

Underwater optical wireless communication (UOWC) has great potential to provide high-speed and intensive communications over short ranges underwater. However, the mobility of the UOWC system is limited by the strict alignment requirements between the transceivers. In this paper, a multi-degree-of-freedom (MDOF) UOWC system with high flexibility and improved transmission performance is proposed and experimentally demonstrated based on the off-the-shelf light-emitting diode (LED) source. A hardware pre-equalization circuit is employed at the transmitter to extend the modulation bandwidth from 5.03 MHz to 50 MHz. At the receiving end, a Fresnel lens array is constructed to achieve efficient convergence of multiple incident optical signals from different directions. To improve the underwater signal transmission quality, we designed an additional digital signal recovery module after the trans-impedance amplifier. Finally, an experimental system is established with a 460 nm blue LED. The communication reliability of the system is verified by the measurement of the eye diagram and the bit error rate of the recovered signal at the receiving end. The experimental results show that optical signals from three different incident directions with a maximum data rate of 100 Mbps are reliably transmitted over a 1.2-m-long water tank using the non-return-to-zero on-off-keying modulation format.

Improved compensation of intrachannel four-wave mixing in dispersion-managed transmission links with mid-span optical phase conjugation

Recently, a novel approach to satisfy the nonlinearity compensation criteria by mid-span optical-phase-conjugation (OPC) was proposed and excellent transmission performance was achieved, there the OPC propagation symmetry was obtained through optimized dispersion management with inverse-dispersion fiber (IDF). In this work, the author numerically investigates the possibilities of using dispersion-compensating fiber (DCF) instead of IDF for construction of the link. Nonlinearity compensation effectiveness of the two links are compared with respected to the intrachannel four-wave mixing (IFWM) induced intensity fluctuation of the ‘1’ bits and the generation of the ghost pulse. It is found that, by launching different powers (or energy of a limited bits) into the spans before and after the OPC, nonlinearity mismatch between the spans of the DCF-managed link can be counterbalanced with significantly improved transmission performance. For a given input energy, there exists an optimum ratio of the energies into the spans before and after the OPC, at which optimum nonlinearity compensation can be achieved, and the compensation effectiveness is very close to that of the IDF-managed link. Further studies show that the optimum energy ratio increases with the input energy but is independent of the span number of the link.

Digital Twin-Driven Vehicular Task Offloading and IRS Configuration in the Internet of Vehicles

Digital mymargin Twin (DT) and Intelligent Reflective Surface (IRS), the most two promising technologies of 6G make the Internet of Vehicles (IoV) more adaptive. However, future autonomous driving needs powerful networking resources and high-quality wireless communications to guarantee the Quality of Service (QoS). Especially considering the time-varying physical operating environments of IoV, it is extremely urgent to improve resource utilization and wireless channel quality. In this work, we propose a Digital Twin-Driven Vehicular Task Offloading and IRS Configuration Framework (DTVIF) to efficiently monitor, learn, and manage the IoV. Specifically, we adopt Mobile Edge Computing (MEC) and IRS to provide augmented computing capacities for vehicles and improve transmission performance when vehicles communicate to MEC servers. DT is employed to achieve real-time data collection and digital representation of physical operating environments of IoV to better support decisions making. In order to reduce the overall delay and energy consumption of DTVIF, we propose a Two-Stage Optimization for Jointly Optimizing Task Offloading and IRS Configuration (TSJTI) algorithm based on Deep Reinforcement Learning (DRL) and Transfer Learning (TFL). In the first stage, we introduce Double Deep <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning Networks (DDQN) to find the optimal offloading decision. In the second stage, based on the parameters learned from the first stage, we migrate the parameters from the first stage to find the optimal IRS configuration based on the Deep Deterministic Policy Gradient (DDPG) method. The simulations demonstrate that the proposed algorithm can effectively reduce the processing latency of task offloading and reduce the average energy consumption in DTVIF.

Efficient MIMO Configuration for Bi-Directional Vertical FSO Link with Multiple Beam Induced Pointing Error

We proposed the statistical misalignment model and the power-efficient configuration of transceivers for bi-directional multi-input and multi-output (MIMO) based vertical free space optical (FSO) links. Spatial diversity based MIMO FSO systems could be used to mitigate atmospheric fading issues. However, the increased number of channels can cause additional pointing error in pointing, acquisition and tracking (PAT) systems. The statistical misalignment model for detecting misalignment error is derived from the multiple transceivers. For the bi-directional characteristics of non-terrestrial back-haul networks, transmission performance is down-leveled to the worse in the asymmetric MIMO configuration of transceivers. The symmetric structure can mitigate the effect of increased pointing error to improve transmission performance. The proposed technique can be applied to the design of power-efficient FSO systems for non-terrestrial wireless back-haul networks.

A Cuckoo Filter-Based Name Resolution and Routing Method in Information-Centric Networking

Information-centric networking (ICN) is a new network architecture that routes content based on names to improve transmission performance. Therefore, the efficiency of name resolution and routing becomes a critical issue in ICN. The bloom filter-based routing scheme has gained significant attention for its ability to improve the memory efficiency of routing nodes in the network, but it cannot handle the movement or deletion of content and has a high false positive rate, which increases bandwidth consumption. In this paper, we propose a cuckoo filter-based name resolution and routing method where resolution requests are forwarded through a hierarchical network structure to the node closest to the content copy as much as possible to minimize latency. This method achieves reliable content removal and allows summaries of content to be exchanged between nodes for resolution error correction and information synchronization based on a modified cuckoo filter. The simulation results show that our method can effectively reduce the number of false positives, and it can reduce the additional overhead caused by processing false positives for a large-scale network by 50% compared with the bloom filter-based scheme.

Research on Reverse Reconstruction Technology of Drive Screws

Reverse innovation design has always been an important topic in the field of engineering. The purpose of this paper is to solve problems in drive screws caused by their complex structure, demand for high shape accuracy and long cycles of design optimization. The reverse modeling technology, the Win 3D scanner and Geomagic Wrap are employed to collect the point cloud data of the drive screw, wrap them into a curved surface, and import them into the reverse engineering software Geomagic Design X to create its highly accurate 3D digital model. In this way, high precision guarantee can be provided for the optimal design of the drive screw, thereby shortening the innovation cycle and laying a technical foundation for the improvement of transmission performance. According to the error analysis software Geomagic Control, the error in the inverse model of the drive screw does not exceed 0.02 mm, which meets the industry’s needs. This model can be used for digital production, and can also provide high-precision model support for secondary innovation, which is of great significance for shortening the product design cycle and enhancing the competitiveness of the enterprise.

Towards Adaptive Network Resource Orchestration for Cognitive Radio Networks

&lt;p&gt;This work proposes an adaptive resource orchestration system for a Wireless Local Area Network (WLAN) that is based on the operating principle of Cognitive Radio (CR) technology. By collecting environmental parameters, including the retransmission rate and the channel occupancy rate, the proposed system has &amp;ldquo;knowledge&amp;rdquo; of overall transmission behavior and can regulate transmission resources. An Adaptive Connection Assignment (ACA) mechanism is proposed for end devices; it find out target end devices with poor transmission performance, analyzes their alternative Access Point (AP) availability and causes them to change connections to improve transmission performance. An Adaptive Channel Utilization (ACU) mechanism is designed for APs to identify a target AP that is suffering from interference, to analyze its alternative channel availability and to require it to change its working channel to improve transmission efficiency. Results of simulations of various scenarios indicate that the throughput of end devices is increased by 15 to 24%, the throughput of APs is increased by 6 to 47% and the retransmission rate of APs is reduced by 0.4 to 5.3%.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Simulation Study of Network Reconfiguration and Load-balancing Method for the Xinjiang Astronomical Observatory Data Center

The Xinjiang Astronomical Observatory Data Center faces issues related to delay-affected services. As a result, these services cannot be implemented in a timely manner due to the overloading of transmission links. In this paper, the software-defined network technology is applied to the Xinjiang Astronomical Observatory Data Center Network (XAO-DCN). Specifically, a novel reconfiguration method is proposed to realise the software-defined Xinjiang Astronomical Observatory Data Center Network (SDXAO-DCN), and a network model is constructed. To overcome the congestion problem, a traffic load-balancing algorithm is designed for fast transmission of the service traffic by combining three factors: network structure, congestion level and transmission service. The proposed algorithm is compared with current commonly load-balancing algorithms which are used in data center to verify its efficiency. Simulation experiments show that the algorithm improved transmission performance and transmission quality for the SDXAO-DCN.

Probabilistically shaped polar-coded MIMO-FSO communication systems with spatially correlated fading.

In this paper, the probabilistically shaped polar-coded multiple-input multiple-output free-space optical (MIMO-FSO) communication system with or without spatially correlated (SC) fading is investigated to improve transmission performance. The designed shaping-polar encoder can flexibly generate three typical shapes of distribution via shaping bits and be decoded in the conventional method. The achievable information rate (AIR) of MIMO-FSO systems with or without SC fading is evaluated to determine the number of shaping bits for the shaping-polar encoder. The non-pairwise distributions are demonstrated to be more suitable for turbulence channels than other distributions. The results show that the AIR of the shaped 4 × 4 systems even exceeds that of the uniform 4 × 5 systems in the low signal-to-noise ratio regions over strong turbulence channels. In terms of bit error rate performance, more than 15 dB shaping gains can be achieved by the shaped 4 × 4 systems compared to the uniform single-input single-output polar-coded systems. In addition, the shaped 4 × 4 systems outperform the uniform ones ranging from 1 dB to 1.9 dB over different atmospheric turbulence channels with or without SC fading, comparable to the uniform MIMO systems with one more physical receiver.