Enhance Transmission Efficiency Research Articles

Self-organized efficient transmission in dynamic networks

Effective protocols are needed to ensure successful transmission of signals, messages, or parcels through a network of intermediaries. Existing studies often overlook real-world constraints such as limited resources. We investigate the design of efficient and robust transmission strategies in dynamic networks subject to dissipation and resource limited node activity, as in wireless sensor networks. We find that dissipation and randomness can enhance transmission efficiency and network resilience. For a broad family of protocols, a self-organized dynamics assures global connectivity, even in the limit of low density of links, where random networks fail.

Electromagnetic performance analysis of a novel radial compound differential field-modulated magnetic gears

Purpose The purpose of this study is to propose a new magnetic gearing device and the proposed transmission model can be applied in the field of wind power and wave energy generation gearboxes. Design/methodology/approach A novel radial differential field-modulated two-stage magnetic gear is introduced, using a unique radial differential linkage to integrate two single-stage magnetic gears. This design incorporates a magnetic isolation ring to enhance transmission efficiency by minimizing magnetic interference and preventing power circulation. The two-stage modulating ring rotor operates synchronously via a connecting bridge, ensuring system stability and efficiency. This configuration not only boosts the gear ratio but also maintains a compact structure, improving power density and efficiency. Leveraging the magnetic field modulation and differential transmission, a finite element model of this gear is developed and its electromagnetic performance is analyzed. Findings The torque density of the new radial differential field-modulated two-stage magnetic gear has increased by 44.97% compared to the traditional tandem two-stage magnetic gear. It achieves a high transmission ratio of 64 and maintains comparable power density, indicating strong torque transfer capabilities suitable for low-speed, high-torque applications. During steady-state operation, the torque pulsation difference between the two stages is minimal, ensuring stable working torque. Social implications This research not only propels the forefront of magnetic gear technology through heightened efficiency and streamlined design but also bears profound societal significance in fostering sustainable energy paradigms. By facilitating superior energy conversion efficiencies in wind turbines and wave energy converters, it plays a pivotal role in mitigating carbon emissions and accelerating the global pivot towards cleaner, renewable energy landscapes. Originality/value A magnetic gear transmission device is proposed, which can achieve high power density and large transmission ratio at the same time, and this study provides a useful reference for the design optimization of new high-performance multistage magnetic gears.

Enhancing Transmission Efficiency in Non-IID Federated Learning by Integrating Quantization into an Existing Trustworthiness Model

Federated Learning (FL) is a distributed machine learning paradigm that enables multiple clients to collaboratively train a model without sharing their data, thus preserving privacy. In this approach, data is not transmitted between the client and the base station. Instead, a model is sent to each device, where it is trained locally, and then retransmitted back to the base station. Each iteration of this process is known as a communication round. However, FL faces challenges, especially when data is not independently and identically distributed (Non-IID). Non-IID data means that the data across clients can vary significantly in distribution, leading to situations where certain classes or features are overrepresented in some clients and underrepresented in others. This lack of uniformity quickly leads to biased model updates and reduced performance. To address this, previous studies have introduced trustworthiness metrics to ensure more reliable model aggregation, minimizing accuracy losses associated with Non-IID data. Another significant challenge is the high transmission load, as model updates between clients and the base station are resource-intensive. Model parameters are transmitted between clients and base stations, which can strain communication channels and slow down the entire process, especially when dealing with larger models and datasets. This communication overhead is a bottleneck that limits the scalability of FL, particularly in resource-constrained environments. Our research addresses these challenges by integrating quantization into the trustworthiness model specifically in a Non-IID scenario. Quantization reduces the precision of model parameters to minimize data transmission, resulting in more efficient communication. We applied different levels of quantization intensity to a model trained on the CIFAR-10 dataset and found that certain methods can significantly reduce transmission overhead without substantial accuracy loss. Our findings suggest that combining quantization with trustworthiness metrics can significantly enhance the efficiency and potentially improve the adoption of Federated Learning in resource-constrained environments.

Buyang huanwu decoction improves synaptic plasticity of ischemic stroke by regulating the cAMP/PKA/CREB pathway

Ethnopharmacological relevanceIschemic stroke is an acute central nervous system disease that poses a threat to human health. It induces a series of severe pathological mechanisms, ultimately leading to neuronal cell death in the brain due to local ischemia and hypoxia. Buyang Huanwu decoction (BYHWD), as a representative formula for treating ischemic stroke, has shown good therapeutic effects in stroke patients. Aim of the studyThis study aimed to explore the mechanism of BYHWD in promoting neural remodeling after ischemic stroke from the perspective of neuronal synaptic plasticity, based on the cAMP/PKA/CREB signaling pathway. Materials and methodsA modified suture technique was employed to establish a rat model of MCAO. The rats were divided into sham, model, and BYHWD (20 g/kg) groups. After the corresponding intervention, rat brains from each group were collected. TMT quantitative proteomics technology was employed for the research. Following proteomics studies, we investigated the mechanism of BYHWD in the intervention of ischemic stroke through animal experiments and cell experiments. The experimental animals were divided into sham, model, and BYHWD (5 g/kg, 10 g/kg, and 20 g/kg) groups. Infarct volume and severity of brain injury were measured by TTC staining. HE staining was utilized to evaluate alterations in tissue morphology. The Golgi staining was used to observe changes in cell body, dendrites, and dendritic spines. Transmission electron microscopy was used to observe the ultrastructure of synapses in the cortex and hippocampus. TUNEL staining was conducted to identify apoptotic neurons. Meanwhile, a stable and reliable (OGD/R) SH-SY5Y cell model was established. The effect of BYHWD-containing serum on SH-SY5Y cell viability was measured by CCK-8 kit. The apoptosis situation of SH-SY5Y cells was determined by Annexin V-FITC/PI. Immunofluorescence was employed to measure the fluorescence intensity of synaptic-related factors Syt1, Psd95, and Syn1. Synaptic plasticity pathways were assessed by using RT-qPCR and Western blot to determine the expression levels of cAMP, Psd95, Prkacb, Creb1/p-Creb1, BDNF, Shank2, Syn1, Syt1, Bcl-2, Bcl-2/Bax mRNA and proteins. ResultsAfter treatment with BYHWD, notable alterations were detected in the signaling pathways linked to synaptic plasticity and the cAMP signaling pathway-related targets among the intervention targets. This trend of change was also reflected in other bioinformatics analyses, indicating the important role of synaptic plasticity changes before and after modeling and drug intervention. The results of vivo and vitro experiments showed that BYHWD improved local pathological changes, and reduced cerebral infarct volume, and neurological function scores in MCAO rats. It increased dendritic spine density, improved synaptic structural plasticity, and had a certain neuroprotective effect. BYHWD increased the postsynaptic membrane thickness, synaptic interface curvature, and synaptic quantity. 10% BYHWD-containing serum was determined as the optimal concentration for treatment. 10% BYHWD-containing serum significantly reduced the overall apoptotic rate of (OGD/R) SH-SY5Y cells. Immunofluorescence experiments demonstrated that 10% BYHWD-containing serum could improve synaptic plasticity and increase the relative expression levels of synaptic-related proteins Syt1, Psd95, and Syn1. BYHWD and decoction-containing serum upregulated the mRNA and protein expression levels in (OGD/R) SH-SY5Y cells and MCAO rats, suggesting its ability to improve damaged neuronal synaptic plasticity and enhance transmission efficiency, which might be achieved through the regulation of the cAMP/PKA/CREB pathway. ConclusionsThis study may provide a basis for clinical medication by elucidating the underlying experimental evidence for the promotion of neural plasticity after ischemic stroke by BYHWD.

Multiscale graph based spatio-temporal graph convolutional network for energy consumption prediction of natural gas transmission process

Energy consumption prediction is crucial for planning and managing natural gas networks, enhancing transmission efficiency. However, most of relevant studies fail to consider the topology of natural gas network. Therefore, a multiscale graph based spatio-temporal graph convolutional network model (MG-STGCN) is proposed, which can extract the multi-scale relationship of data. Based on graph convolutional and temporal gated convolution network, data correlation, spatial dependence and time dependence are considered to improve the prediction effect. In order to verify the effect of the model, the model is tested based on two natural gas networks in different region of China. The single step prediction results of MG-STGCN are significantly better than spatio-temporal graph convolutional network (STGCN), long short-term memory (LSTM), gate recurrent unit (GRU) and temporal convolutional network (TCN), which proves the effectiveness and generalization ability of MG-STGCN. The training time of MG-STGCN is reduced by 20.8 % compared with STGCN. In different prediction time step, the average prediction effect of MG-STGCN is better than that of other methods, and the performance improvement rate is not less than 8.6 %. MG-STGCN can well capture the parameter correlation, spatial and temporal dependence of data, and achieve effective prediction of pipeline energy consumption.

Enhancing transmission and coupling efficiency in Cassegrain optical systems with four-petal Gaussian beams

This study investigates the propagation characteristics of four-petal Gaussian beams through Cassegrain optical systems. With the properties of this new type of beam, the problem of masking loss in the Cassegrain optical system can be well solved. Analytical expressions of the optical field for beams after propagating the optical system are derived, considering atmospheric turbulence effects and random jitter on coupling efficiency. Simulation tests explore various factors affecting transmission efficiency. Acceptable axial mounting error tolerance and significant efficiency drops beyond specific communication distances under different turbulence conditions (). Achieving over 80 coupling efficiency is feasible with specific lens parameters (f = 0.4 − 0.5 mm, receiving position after 4 km) using standardized lenses. This work offers a theoretical model for four-petal Gaussian beams, aiding optical communication design and application, with practical engineering implications.

Parallel event-triggered dynamic output feedback control for nonlinear networked systems with randomly occurring multiple communication delays

This paper investigates the issue of parallel event-triggered (PET) dynamic output feedback control for networked control systems (NCSs) built by the discrete-time T–S fuzzy model. Initially, a novel PET dynamic output feedback controller is designed. Based on saving network resources and enhancing transmission efficiency, the PET strategy makes full use of relative and absolute triggering condition information. And the dynamic output feedback control can not only address unmeasurable states but also provide a better response to the internal information of the system. The random multiple communication delays and the ℓth-order Rice fading model with different channel coefficients, meanwhile, are both applied in the system. It is closer to the actual situation. Subsequently, new sufficient conditions of membership function dependence are proposed via the staircase function approximation method combined with Lyapunov stability. It guarantees that the system is exponentially mean square stable (EMSS) with H∞ performance. Ultimately, the presented results are validated using two examples. In the future, we will explore the correlative research of T–S fuzzy Markov jump NCSs.

Genomic surveillance and serological profile of SARS-CoV-2 variants circulating in Macaé and nearby cities, southeastern Brazil.

A characteristic of the COVID-19 pandemic has been the sequential emergence and global dissemination of SARS-CoV-2 variants, noted for their enhanced transmission efficiency. These variants with mutations in the Spike glycoprotein (S-glycoprotein), which interacts with ACE2 receptors in human cells is critical for infection, affects the transmissibility of the virus, which is a matter of great concern for public health. This research analyses the effects these variants on a cohort of vaccinated and naturally infected individuals from the cities of Macaé-RJ, Rio das Ostras-RJ, and Campos dos Goytacazes-RJ, Brazil, from March 2021 to March 2023. This investigation encompasses the Alpha (B.1.1.7), Gamma (P.1), Delta (B.1.617.2, B.1.671.3), and Omicron (BQ.1, BQ.1.1 sublines, and BF.7) variants, focusing on their genomic surveillance and implications for the disease's epidemiology. The experimental analysis included a control group (vaccinated and uninfected subjects), and an infected group (post-vaccinated subjects). Samples from nasopharyngeal swabs underwent viral detection via RT-qPCR for diagnosis confirmation. RNase H-dependent RT-qPCR (rhAmp-PCR) and third-generation sequencing were used to detect SARS-CoV-2 variants. Anti-S-glycoprotein immunoglobulins were also evaluated for vaccinated infected and noninfected volunteers. Symptoms from infected individuals were compiled in order to reveal patterns of clinical signs associated with viral infection. The study included 289 participants, with infections identified by Gamma (n = 44), Delta (n = 189), and Omicron (n = 56) variants. The prevalent symptoms among the naturally infected participants were cough, fever, sore throat, headache, and runny nose. For Omicron, cognitive symptoms such as memory loss and concentration issues were reported. Interestingly, the infected vaccinated group had higher anti-S-glycoprotein IgM production (n = 28, 0.2833 ± 0.09768 OD) compared to the uninfected vaccinated group (n = 14, 0.1035 ± 0.03625 OD). Conversely, anti-S-glycoprotein IgG production was higher in the control group (n = 12, 1.770 ± 0.1393 OD) than in the infected vaccinated group (n = 26, 1.391 ± 0.1563 OD). This comprehensive study enables monitoring of predominant variants and their correlation with clinical cases, providing valuable insights for public health. Our research group continues to survey circulating variants, contributing to the global understanding of the pandemic.

Quantization-Based Event-Triggered H∞ Consensus for Discrete-Time Markov Jump Fractional-Order Multiagent Systems with DoS Attacks

This paper investigates the H∞ consensus problem of discrete-time Markov jump fractional-order multiagent systems (DTMJFOMASs) under denial-of-service (DoS) attacks. By applying the short-memory principle, we can obtain discrete-time Markov jump multiagent systems with partially unknown probabilities. A novel quantized event-triggering mechanism (QETM), based on a mode-dependent logarithmic quantizer, is proposed to enhance transmission efficiency among multiagents. A distributed controller with quantized output is developed. Sufficient conditions are provided to ensure the system achieves H∞ consensus through Lyapunov stability theory. Finally, two examples are given to verify the effectiveness of the proposed model.

Overview of RIS-enabled secure transmission in 6G wireless networks

As the 6th-Generation (6G) wireless communication networks evolve, privacy concerns are expected due to the transmission of vast amounts of security-sensitive private information. In this context, a Reconfigurable Intelligent Surface (RIS) emerges as a promising technology capable of enhancing transmission efficiency and strengthening information security. This study demonstrates how RISs can play a crucial role in making 6G networks more secure against eavesdropping attacks. We discuss the fundamentals and standardization aspects of RISs, along with an in-depth analysis of Physical-Layer Security (PLS). Our discussion centers on PLS design using RIS, highlighting aspects including beamforming, resource allocation, artificial noise, and cooperative communications. We also identify the research issues, propose potential solutions, and explore future perspectives. Finally, numerical results are provided to support our discussions and demonstrate the enhanced security enabled by RIS.

Dual-Frequency Impedance Matching Network Design Using Genetic Algorithm for Power Ultrasound Transducer.

Dual-frequency ultrasounds have demonstrated significant potential in augmenting thermal ablation efficiency for tumor treatment. Ensuring proper impedance matching between the dual-frequency transducer and the power amplifier system is imperative for equipment safety. This paper introduces a novel dual-frequency impedance matching network utilizing L-shaped topology and employing a genetic algorithm to compute component values. Implementation involved an adjustable capacitor and inductor network to achieve dual-frequency matching. Subsequently, the acoustic parameters of the dual-frequency HIFU transducer were evaluated before and after matching, and the effects of ultrasound thermal ablation with and without matching were compared. The proposed dual-frequency impedance matching system effectively reduced the standing wave ratio at the two resonance points while enhancing transmission efficiency. Thermal ablation experiments with matching circuits showed improved temperature rise efficiencies at both frequencies, resulting in an expanded ablation zone. The dual-frequency impedance matching method significantly enhances the transmission efficiency of the dual-frequency ultrasound system at two operational frequencies, thereby ensuring equipment safety. It holds promising prospects for application in dual-frequency ultrasound treatment.

Characteristics of Resting-State Electroencephalogram Network in α-Band of Table Tennis Athletes.

Table tennis athletes have been extensively studied for their cognitive processing advantages and brain plasticity. However, limited research has focused on the resting-state function of their brains. This study aims to investigate the network characteristics of the resting-state electroencephalogram in table tennis athletes and identify specific brain network biomarkers. A total of 48 healthy right-handed college students participated in this study, including 24 table tennis athletes and 24 controls with no exercise experience. Electroencephalogram data were collected using a 64-conductive active electrode system during eyes-closed resting conditions. The analysis involved examining the average power spectral density and constructing brain functional networks using the weighted phase-lag index. Network topological characteristics were then calculated. The results revealed that table tennis athletes exhibited significantly higher average power spectral density in the α band compared to the control group. Moreover, athletes not only demonstrated stronger functional connections, but they also exhibited enhanced transmission efficiency in the brain network, particularly at the local level. Additionally, a lateralization effect was observed, with more potent interconnected hubs identified in the left hemisphere of the athletes' brain. Our findings imply that the α band may be uniquely associated with table tennis athletes and their motor skills. The brain network characteristics of athletes during the resting state are worth further attention to gain a better understanding of adaptability of and changes in their brains during training and competition.

Multi-image optical information hiding algorithm based on Fourier transform without hidden keytransmission.

Existing optical information hiding algorithms for multiple images require generating hidden keys for embedded images, resulting in the transmission of numerous keys. This challenge undermines the usage of these algorithms in low-quality networks. To address this issue and enhance transmission efficiency, we present a multi-image optical information hiding algorithm based on Fourier transformation principles, which is employed to generate hidden frequency maps and carrier frequency maps. Specific low-frequency information zones are extracted within these hidden frequency maps. A chaotic system integrates a phase mask, modulated with the low-frequency regions, positioned in the carrier frequency map's high-frequency sector. The final stego image is obtained by subjecting the carrier frequency map to inverse Fourier transformation. Experimental analysis shows that concealing three images takes only 0.0089s, with extraction requiring 0.0658s. Post-extraction PSNR values for hidden images exceed 32dB. Robustness and anti-attack experiments were done to prove the security of this algorithm. The compared experiments between the proposed method and other state-of-the-art algorithms affirm the algorithm's attributes of simplicity, ease of implementation, robust security, and high efficiency. Importantly, the restoration process eliminates the necessity of transmitting hidden keys, reducing network burdens and enhancing both concealment and extraction efficiencies significantly.

Unlocking the Ocean 6G: A Review of Path-Planning Techniques for Maritime Data Harvesting Assisted by Autonomous Marine Vehicles

Seamless integration of both terrestrial and non-terrestrial networks is crucial to providing full-dimensional wireless and ubiquitous coverage, particularly catering to those engaged in marine activities. Compared to terrestrial networks, wireless communications in the marine domain are still not satisfactory for ubiquitous connectivity. Featuring agile maneuverability and strong adaptive capability, autonomous marine vehicles (AMVs) play a pivotal role in enhancing communication coverage by relaying or collecting data. However, path planning for maritime data harvesting is one of the most critical issues to enhance transmission efficiency while ensuring safe sailing for AMVs; yet it has rarely been discussed under this context. This paper provides a comprehensive and holistic overview of path-planning techniques custom-tailored for the purpose of maritime data collection. Specifically, we commence with a general portrayal of fundamental models, including system architectures, problem formulations, objective functions, and associated constraints. Subsequently, we summarize the various algorithms, methodologies, platforms, tools, coding environments, and their practical implementations for addressing these models. Furthermore, we delve into the burgeoning applications of path planning in the realm of maritime data harvesting and illuminate potential avenues for upcoming research endeavors. We believe that future research may focus on developing techniques to adapt more intricate and uncertain scenarios, such as sensor failures, inaccurate state estimations, complete modeling of communication channels, ocean dynamics, and application of heterogeneous systems.

Topological light transport in low-symmetry valley photonic crystals

Valley photonic crystals represent a cornerstone in the field of topological photonics, which promotes the development of cutting-edge photonic waveguides. These waveguides support robust transmission by using valley-dependent edge states. This innovation marks a great leap forward in enhancing transmission efficiency, (especially in sharp bends), thus opening up a new way for efficient optical information transmission. However, although the role of symmetry in topology and photonic crystals cannot be exaggerated, it is worth noting that valley photonic crystals provide a unique platform for exploring the interplay between symmetry and topological phenomena. An intriguing analogy between valley photonic crystals and the quantum valley Hall effect is an example, which will be shown when the symmetry of spatial inversion is broken. At present, the characteristic of most valley photonic crystals is <i>C</i><sub>3</sub>-rotational symmetry, which leads to an interesting study, that is, whether crystals with lower symmetry can also support topological light transmission. In order to solve this problem head-on, our work focuses on constructing and characterizing valley photonic crystals with low symmetry by carefully adjusting the unit cell morphology. Through theoretical analysis and numerical simulation, we unveil the remarkable ability of these low-symmetry valley photonic crystals to facilitate topological light transport. Initially, we analyze the bulk bands of these low-symmetry crystals, observing a narrowed photonic band gap and a shift in the irreducible Brillouin zone compared with <i>C</i><sub>3</sub>-rotation symmetric crystals. To examine edge state transmission, we calculate dispersion relations and electric field distributions, revealing two edge states with opposite phase chirality at the same frequency. Using this point, we achieve unidirectional excitation of edge states. Additionally, we manipulate the refractive index of the surrounding medium and explore various scenarios of external light beam coupling. Moreover, we investigate the robust transmission of edge states, demonstrating smooth passage of light through sharp corners in <i>Z</i>-shaped bend waveguides without backscattering. In conclusion, our findings underscore the pivotal role played by edge states in facilitating unidirectional excitation and robust transmission in low-symmetry valley photonic crystals. By enriching the diversity of topological photonic structures and providing valuable insights into the behavior of topological light transport in structures with lower symmetry, our work contributes to the ongoing quest for novel photonic platforms with enhanced functions and performance.

Application of singular value decomposition in image compression

The field of computer graphics and multimedia technologies places significant emphasis on the study path of image compression. The increasing prevalence of digital photographs has led to a significant growth in the size of image files, hence presenting storage and transmission issues. The Singular Value Decomposition (SVD) is a matrix decomposition method that possesses significant computational capacity and finds utility in various domains. In recent years, SVD has emerged as a prevalent technique in the domain of image processing, particularly in the context of image compression, yielding notable outcomes. This work primarily employs a literature review research methodology to explore the application of SVD. Specifically, SVD is applied to image compression using Matlab. The approach holds significance due to its ability to achieve efficient compression of digital images, hence enhancing transmission efficiency without compromising image quality.

Asynchronous Pattern-Designed Channel Access Protocol in Underwater Acoustic Wireless Sensor Networks

Due to the significant propagation delay in underwater sensor networks, conflict retransmission in channel access protocols comes at a high cost. This poses a challenge in scenarios where multiple sensor nodes generate data frames with strong temporal correlations, such as in disaster warning applications. Traditional channel allocation and timeout-based retransmission mechanisms lead to considerable access delays, making it difficult to meet the requirements. To tackle this issue, we propose the asynchronous pattern-designed random access (APDRA) protocol. This protocol enhances the access probability by designing retransmission time intervals for data frames based on pattern design. Additionally, we introduce a successive interference cancellation (SIC) mechanism at the receiver for decoding. This mechanism facilitates the transformation of the conventional method of discarding conflicted data frames into iterative decoding, thereby enhancing transmission efficiency. Via the utilization of simulations, we compare the APDRA protocol conventional underwater medium access control (MAC) protocols and existing retransmission mechanisms. The results demonstrate that the APDRA protocol has the ability to improve both the transmission success ratio (TSR) and reduces the access delay to some extent.

Secure and efficient data transmission based on quantum dialogue with hyperentangled states in cloud office

Quantum cloud computing facilitates the rapid processing of vast amounts of data and provides robust network services. To enhance the efficiency and security of data transmission in cloud computing, this study proposes a quantum cloud office model and presents a novel quantum dialogue protocol particularly designed for this model. The proposed protocol enables authenticated communication among office correspondents. Leveraging the unique properties of multiple quantum degrees of freedom, this protocol expands the channel capacity and enhances transmission efficiency. Compared to previous measurement-device-independent quantum dialogue (MDI-QD) protocols, this protocol not only further improves transmission efficiency but also enables mutual authentication between correspondents, effectively defending against the risk of man-in-the-middle attacks. The utilization of a cross-Kerr medium for Bell measurements offers a 50% increase in efficiency compared to linear optical equipment. This novel protocol is tailored to the data transmission process within the cloud office model, thereby enhancing the communication efficiency and reinforcing secure data transmission.

An Novel Technique for Wireless Power Transfer Systems Based on the Parametric Set

High efficiency and high power level are important indicators of the Inductive Power Transfer (IPT) system. However, the problem of optimizing the efficiency of IPT has not been well solved, and improving control methods is one of the effective ways to solve this problem. In order to improve the existing secondary-side control scheme and achieve the goal of simultaneously achieving optimal transmission efficiency and stable output, this paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. This paper suggests a parameter-based control strategy for coordinating the primary and secondary sides in the IPT system. The paper develops a model of the IPT system to find the best load, then analyzes the active rectifier, phase shift angle, and load relationship to create a parameter set for controlling quantities like α and β. From there, a new control strategy is proposed and its effectiveness is confirmed through simulations. The proposed strategy enhances transmission efficiency and output stability of the IPT system, making it highly applicable in areas like electric vehicle charging and wireless charging devices.

A Genetic Algorithm Based Scheduling Method With Carrier Aggregation In 5G Networks

Abstract Recently, 5G has become an important mechanism to serve a growing amount of users in the network, which provides enhanced mobile broadband (eMBB) and ultra-reliable low latency communications (URLLC) services. Meanwhile, Carrier Aggregation (CA) is a promising 5G technology which lets users aggregate fragmented carrier components (CCs) to enhance transmission efficiency. This paper addresses 5G scheduling problem in the CA-enabled eMBB–URLLC coexistence network using genetic algorithm (GA). The key idea is first treating the serving time slot of each user equipment (UE) as a gene and combining all genes to form a chromosome, then generating a set of chromosomes as the initial population and letting the population evolve to iteratively select the fittest one as the resource scheduling decision. Up to now, this is the first work using GA for solving the scheduling problem in the CA-enabled 5G network. Simulation results show that our proposed method can serve the most UEs ($4\%$) with the highest throughput ($7.1\%$) and lower delay.