Network Performance Research Articles

The role of a structured community health worker network in achieving malaria elimination goals in the Dominican Republic: An impact evaluation amid COVID-19 disruptions.

Community health workers (CHWs) can play a key role in malaria elimination through expanding access to malaria testing and treatment. The Dominican Republic, a low endemic country committed to eliminate malaria by 2025, implemented a structured malaria CHW network in the three main urban foci of Greater Santo Domingo. This research quantifies the networks' contributions towards malaria elimination from its implementation in 2019 until 2022. The study highlights the impact of COVID-19 disruptions on CHWs' performance and explores the network's ability to bounce back from health systems' shocks. The performance of the malaria CHW network was evaluated using weekly data collected from CHWs and routinely collected surveillance data from the Ministry of Public Health (MoH). We assess performance of CHWs by analyzing key variables including (1) reporting compliance, (2) household visitation rates, (3) malaria rapid diagnostic tests performed, (4) malaria cases detected, and (5) time between symptom onset and malaria diagnosis. To evaluate the impact of COVID-19 on the network, CHW's performance indicators are evaluated across three time periods (prior to, during, and after COVID-19 interruptions). Over the evaluation period, reported malaria cases in study foci decreased from 1,243 cases in 2019 to 6 cases in 2022. CHWs diagnosed and treated over 43% of malaria cases in study foci before COVID-19 interruptions and 14% during interruptions. 83% of cases detected by CHWs were detected through active case detection, with 17% detected passively. CHWs detected malaria cases and initiated treatment 1.5 days earlier than health facilities and MoH personnel performing active case detection. This evaluation provides evidence that a structured CHW network with clearly defined responsibilities and management protocol can help curb local malaria transmission. It adds to a growing body of research on the feasibility and benefits of CHW-led proactive household visitation.

PT-HMC: Optimization-based Pre-Training with Hamiltonian Monte-Carlo Sampling for Driver Intention Recognition

Driver intention recognition (DIR) methods mostly rely on deep neural networks (DNNs). To use DNNs in a safety-critical real-world environment it is essential to quantify how confident the model is about the produced predictions. Therefore, this study evaluates the performance and calibration of a temporal convolutional network (TCN) for multiple probabilistic deep learning (PDL) methods (Bayes-by-Backprop, Monte-Carlo dropout, Deep ensembles, Stochastic Weight averaging - Gaussian, Multi SWA-G, cyclic Stochastic Gradient Hamiltonian Monte Carlo). Notably, we formalize an approach that combines optimization-based pre-training with Hamiltonian Monte-Carlo (PT-HMC) sampling, aiming to leverage the strengths of both techniques. Our analysis, conducted on two pre-processed open-source DIR datasets, reveals that PT-HMC not only matches but occasionally surpasses the performance of existing PDL methods. One of the remaining challenges that prohibits the integration of a PDL-based DIR system into an actual car is the computational requirements to perform inference. Therefore, future work could focus on optimizing PDL methods to be more computationally efficient without sacrificing performance or the ability to estimate uncertainties.

Unmanned-Aerial-Vehicle-Assisted Secure Free Space Optical Transmission in Internet of Things: Intelligent Strategy for Optimal Fairness

In this article, we consider an UAV (unmanned aerial vehicle)-assisted free space optical (FSO) secure communication network. Since FSO signal is impossible to detect by eavesdroppers without proper beam alignment and security authentication, a BS employs FSO technique to transfer information to multiple authenticated sensors, to improve the transmission security and reliability with the help of an UAV relay with decode and forward (DF) mode. All the sensors need to first send information to the UAV to obtain security authentication, and then the UAV forwards corresponding information to them. Successive interference cancellation (SIC) is used to decode the information received at the UAV and all authenticated sensors. With consideration of fairness, we introduce a statistical metric for evaluating the network performance, i.e., the maximum decoding outage probability for all authenticated sensors. In particular, applying an intelligent approach, we obtain a near-optimal scheme for secure transmit power allocation. With a well-trained allocation scheme, approximate closed-form expressions for optimal transmit power levels can be obtained. Through some numerical examples, we illustrate the various design trade-offs for such a system. Additionally, the validity of our approach was verified by comparing with the result from exhaustive search. In particular, the result with DRL was only 0.3% higher than that with exhaustive search. These results can provide some important guidelines for the fairness-aware design of UAV-assisted secure FSO communication networks.

Heterogeneous conservation corridors of remnant vegetation protect biodiversity in South African timber mosaics

AbstractIt is urgent now to place greater emphasis on harmonising conservation of indigenous biodiversity with food and fibre production. This is especially important in countries like South Africa which support high levels of irreplaceable biodiversity. The local timber industry has responded to this challenge by retaining large-scale networks of conservation corridors of historic ecosystems in the forestry landscape. The corridors consist mostly of grassland, with patches of indigenous forest, thickets, wetlands, ponds, and rivers. The motivation is to future proof compositional and functional biodiversity for ecological resilience in these production environments in a rapidly changing world. We synthesise here the substantial evidentiary research on the effectiveness of conservation corridors in plantation forestry-dominated landscapes in the Maputaland-Pondoland-Albany biodiversity hotspot. We focus on six emergent themes: 1. corridor dimensions, orientation, and connectivity, 2. heterogeneity at different spatial scales, 3. maintaining aquatic and terrestrial habitat quality in the conservation corridors, 4. biodiversity value of conservation corridors relative to protected areas, 5. the plantation matrix, and 6. assessment of corridor network performance. Results show the importance of prioritising large, high quality conservation corridors, especially those with a high number of natural features and variety of environmental conditions, both terrestrial and aquatic. Alien clearing, grazing control, and appropriate fire regimes should be prioritised in these corridors. Where possible we need to retain, restore, or replicate the natural ecological regimes. Overall, this conservation approach in commercial forestry landscapes helps to conserve indigenous biodiversity and ecosystem integrity, improves connectivity across afforested landscapes while also having sustainable timber production, thereby safeguarding the resilience of these working landscapes well into the future.

The Effect of Processing Techniques on the Classification Accuracy of Brain-Computer Interface Systems

Background/Objectives: Accurately classifying Electroencephalography (EEG) signals is essential for the effective operation of Brain-Computer Interfaces (BCI), which is needed for reliable neurorehabilitation applications. However, many factors in the processing pipeline can influence classification performance. The objective of this study is to assess the effects of different processing steps on classification accuracy in EEG-based BCI systems. Methods: This study explores the impact of various processing techniques and stages, including the FASTER algorithm for artifact rejection (AR), frequency filtering, transfer learning, and cropped training. The Physionet dataset, consisting of four motor imagery classes, was used as input due to its relatively large number of subjects. The raw EEG was tested with EEGNet and Shallow ConvNet. To examine the impact of adding a spatial dimension to the input data, we also used the Multi-branch Conv3D Net and developed two new models, Conv2D Net and Conv3D Net. Results: Our analysis showed that classification accuracy can be affected by many factors at every stage. Applying the AR method, for instance, can either enhance or degrade classification performance, depending on the subject and the specific network architecture. Transfer learning was effective in improving the performance of all networks for both raw and artifact-rejected data. However, the improvement in classification accuracy for artifact-rejected data was less pronounced compared to unfiltered data, resulting in reduced precision. For instance, the best classifier achieved 46.1% accuracy on unfiltered data, which increased to 63.5% with transfer learning. In the filtered case, accuracy rose from 45.5% to only 55.9% when transfer learning was applied. An unexpected outcome regarding frequency filtering was observed: networks demonstrated better classification performance when focusing on lower-frequency components. Higher frequency ranges were more discriminative for EEGNet and Shallow ConvNet, but only when cropped training was applied. Conclusions: The findings of this study highlight the complex interaction between processing techniques and neural network performance, emphasizing the necessity for customized processing approaches tailored to specific subjects and network architectures.

Porosity identification using residual PPTransformer network

Precisely estimating the carbonate’s porosity is essential for subsurface reservoir characterization. However, conventional methods for obtaining porosity using either core measurements or logging interpretation are expensive and inefficient. Considering the sequence data feature of logging curves and the booming development of intelligent networks in geoscience, this study proposes a reliable and low-cost intelligent Porosity Prediction Transformer (PPTransformer) framework for reservoir porosity prediction using logging curves as inputs. PPTransformer network not only extracts global features through convolutional layers but also captures local features using Encoders and self-attention mechanisms. This proposed network is a data-driven supervised learning framework with a superior accuracy and robustness. The testing results demonstrate that compared to the Transformer network, Long Short-Term time series network, and support vector machine method, the PPTransformer framework exhibits the highest average correlation coefficient and determination coefficient indicators and the lowest root mean square error and absolute error indicators. Moreover, adding stratigraphic lithology as geological constraints to the PPTransformer framework further improves the prediction performance. This indicates that geological constraints will enhance network performance.

Significant Advances in Application Resiliency: The Data Engineering Perspective on Network Performance Metrics

Purpose: The purpose of the article is to explore how network metrics like latency, packet loss, and throughput, combined with application logs, can help organizations improve the reliability and performance of their applications. It focuses on how these insights support Site Reliability Engineering (SRE) teams in proactively addressing issues to achieve better application resiliency, enhancing user experience and market trust. Methodology: The article uses a combination of case studies and analysis to demonstrate how monitoring specific network metrics and application logs helps identify and resolve performance issues. It examines real-world scenarios where proactive adjustments based on these metrics improved application reliability and aligned with organizational objectives. Findings: The findings show that by analyzing network metrics and application logs, organizations can pinpoint causes of transaction failures, such as high latency, packet loss, or misconfigured firewalls. Proactive resolutions based on these insights result in smoother application performance, reduced downtime, and increased user satisfaction. Unique Contribution to theory, practice and policy: This article makes valuable contributions to theory, practice, and policy. For theory, it expands the understanding of how network metrics and application logs can work together to improve application resiliency, offering a framework for integrating Site Reliability Engineering (SRE) principles with network observability. For practice, it provides clear, actionable steps for SRE teams to identify and resolve performance issues, helping organizations enhance reliability and user satisfaction. For policy, it highlights the importance of proactive network monitoring and metric-driven decision-making, encouraging organizations to adopt policies that prioritize resiliency, ensure consistent performance, and meet service-level agreements (SLAs).

Steered Bubble: An Interposer-based Deadlock Recovery Algorithm for Multi-chiplet Systems

Dividing a single System-on-Chip (SoC) into multiple chiplets and integrating them via an interposer can achieve an optimal balance between continuous transistor integration and monetary cost. However, potential deadlock may arise between the chiplets and the interposer. This deadlock can be avoided by applying turn restriction or injection control on the boundary routers, at the cost of additional latency and sub-optimal performance. Compared to deadlock avoidance, deadlock recovery exerts less impact on network performance. Nevertheless, accurate and timely deadlock detection, along with efficient deadlock recovery, continues to pose significant challenges. Additionally, modularity is a specific concern, which involves integrating chiplets of various functions, sizes, manufacturing processes, and so on. Minimizing the negative impact of deadlock resolution while maximizing modularity is crucial for achieving the benefit of chiplets. This paper proposes a modular deadlock detection strategy, Up-Down, which monitors both the upward and downward directions of vertical channels, facilitating information exchange through the congestion-sense network. When a pair of blocked upward and downward vertical channels is detected simultaneously, it is considered that an inter-chiplet deadlock has occurred. This significantly enhances the accuracy of deadlock detection by two orders of magnitude compared to time-out deadlock detection. Furthermore, this paper introduces Steered Bubble, a low-cost deadlock recovery algorithm. It does so by injecting bubbles into potential deadlock cycles identified by Up-Down. These bubbles follow preset paths, ensuring efficient deadlock recovery. Experimental results indicate that the Steered Bubble results in an average performance enhancement of \(1\% \sim 10\% \) during full-system simulations, with an area overhead of less than 2%.

An Effective Congestion and Interference Secure Routing Protocol for Internet of Things Applications in Wireless Sensor Network

AbstractThis paper provides an effective Wireless Sensor Network (WSN) routing solution for Internet of Things (IoT) applications cognizant of congestion, security, and interference. Because several sources try to deliver their packets to a destination simultaneously, which is a common case in IoT applications. The proposed congestion and interference aware safe routing protocol is claimed to work in networks with high traffic. The signal to interference ratio (SINR), congestion level, and survival factor is used in our suggested procedure to estimate the cluster head selection factor first. The adaptive fuzzy c-means clustering method clusters the network nodes based on the cluster head selection factor. After that, data packets are encrypted using Adaptive Quantum Logic-based packet coding. Finally, the Adaptive Krill Herd (AKH) optimization method identifies the least congested corridor, resulting in optimal data transmission routing. The exploratory findings show that the provided strategy outperforms previous methodologies in network performance, end-to-end delay, packet delivery ratio, and node remaining energy level.

Performance Analysis and Design Principles of Wireless Mutual Broadcast Using Heterogeneous Transmit Power for Proximity-Aware Services

As proximity-aware services among devices such as sensors, IoT devices, and user equipment are expected to facilitate a wide range of new applications in the beyond 5G and 6G era, managing heterogeneous environments with diverse node capabilities becomes essential. This paper analytically models and characterizes the performance of heterogeneous random access-based wireless mutual broadcast (RA-WMB) with distinct transmit (Tx) power levels, leveraging a marked Poisson point process to account for nodes’ various Tx power. In particular, this study enables the performance of RA-WMB with heterogeneous Tx power to be represented in terms of the performance of RA-WMB with a common Tx power by deriving an equivalent Tx power based on the probability distribution of heterogeneous Tx power and the path loss exponent. This approach allows for an analytical and quantitative comparison of heterogeneous RA-WMB performance with the common Tx power configuration. Further, the study derives performance ratios among node groups with distinct Tx power levels and formulates an optimization problem to design a heterogeneous Tx power configuration that balances individual node group performance improvements with overall network performance, yielding the optimal Tx power configuration. A closed-form suboptimal transmission probability (TxPr) is also proposed to improve heterogeneous RA-WMB performance, providing an efficient alternative to iterative methods for the optimal TxPr. Numerical results demonstrate the accuracy of performance analysis and highlight the effectiveness of the proposed designs.

A Method for Detecting Small Target Weld Defects Based on Feature Reorganization Network

Abstract In the process of intelligent weld seam inspection, small weld seams are often prone to false detections or omissions. Existing methods simply concatenate feature segments during the upsampling process without analyzing the importance of each feature segment, leaving room for further improvement in detection accuracy. To address these issues, this study proposes a Feature Reorganization Network (FRNet) for detecting small target weld defects. First, the C2f-Faster-EMA feature extraction module is designed using GSConv convolution, and the LSKNet is introduced to dynamically adjust the receptive field of the backbone in the Backbone section, enhancing the model's ability to extract small target features. Then, a lightweight CARAFE upsampling module is designed in the neck network, which retains more detailed information through feature reorganization and feature expansion, and introduces the parameter-free attention mechanism SimAM to fully capture the contextual information of small targets, thereby enhancing the proposed model's ability to extract small target features. Finally, the GIoU boundary loss function is used to improve the network's bounding box regression performance, achieving intelligent detection of small target weld defects. Experimental results show that the proposed method achieves a mean average precision, parameter count, and computation volume of 85.6%, 2.5M, and 7.0G, respectively, for weld defect detection, outperforming the comparison models and meeting the requirements of practical engineering applications.

Strategic Traffic Management in Mixed Traffic Road Networks: A Methodological Approach Integrating Game Theory, Bilevel Optimization, and C-ITS

The integration of Connected Vehicles into conventional traffic systems presents significant challenges due to the diverse behaviors and objectives of different drivers. Conventional vehicle drivers typically follow User Equilibrium principles, aiming to minimize their individual travel times without considering the overall network impact. In contrast, Connected Vehicle drivers, guided by real-time information from central authorities or private service providers, can adopt System Optimum strategies or Cournot-Nash oligopoly behaviors, respectively. The coexistence of these distinct player classes in mixed-traffic environments complicates the task of achieving optimal traffic flow and network performance. This paper presents a comprehensive framework for optimizing mixed-traffic road networks through a multiclass traffic assignment model. The framework integrates three distinct types of players: conventional vehicle drivers adhering to User Equilibrium principles, Connected Vehicle drivers following System Optimum principles under a central governing authority, and Connected Vehicle drivers operating under Cournot-Nash oligopoly conditions with access to services from private companies. The methodology includes defining a model to achieve optimal mixed equilibria, designing an algorithm for multiclass traffic assignment, formulating strategic games to analyze player interactions, and establishing key performance indicators to evaluate network efficiency and effectiveness. The framework is applied to a real-world road network, validating its practicality and effectiveness through computational results. The extraction and analysis of computational results are used to propose optimal traffic management policies for mixed-traffic environments. The findings provide significant insights into the dynamics of mixed traffic networks and offer practical recommendations for improving traffic management in increasingly complex urban transportation systems.

Evaluating the Impact of Latency in a Virtual Private Network Performance Using Different Encryption Algorithm and Hashing

This study analyzes the performance of a network, specifically on advanced encryption standard and Triple data encryption standard when used with secure hash algorithm and Message digest-5 hashing algorithms, when the communication between two communicating nodes is aided by virtual private network connectivity. The study involved two cases; one with AES and the other with 3DES at which each case had a set of two test scenarios which simulated the performance of the selected protocols to simulate the intended scenarios. The simulation scenarios were configuring AES256 with SHA256, AES256 with MD5 and 3DES with SHA256 as well as 3DES with MD5. The simulations carried out measured the latency that impacted the VPN network. Data was collected using network simulation tool “the Ping tool” in an Ubuntu 20.0.1 environment. It is founded that the hashing algorithm SHA256 experienced high Latency compared to MD5 which is contributed by the capacity of processing the hash value by SHA256. Sha256 Produces a hash value that is 266 bits long while MD5 produces a hash value that is 128 bits long, the longer the hash value the longer time is taken to process it. This causes more latency in the network

INVESTIGATION OF THE MANET NETWORK PERFORMANCE CONCERNING DIFFERENT ROUTING PROTOCOLS

Mobile Ad-Hoc Network is a decentralized organization that operates without a foundation. Due to the Mobile Ad-Hoc Network's self-configurable and simple organizational component, many applications may be run. Because of this, various applications are available. If helpful guidelines are established, Mobile Ad-Hoc Network will become dependable. We shall research the organization's competent steering convention for hypertext transfer protocol traffic. We must reach this conclusion with accuracy since this will be the main focus of our inquiry. Latency and throughput were employed to achieve show research goals. For this work reenactment inquiry, your expectations must be based on the conventions it chose since they performed better on all four perspectives. After analyzing its needs, an organization may improve its operations by choosing better conventions. This may boost an organization's efficiency. This research examined AODV, DSR, and OLSR routing methods. This study used OPNET Modeler 14.5 to enhance ad-hoc network performance.

Network Performance Monitoring and Diagnostic Analysis in Site Reliability Engineering Practices

Network Performance Monitoring and Diagnostic Analysis in Site Reliability Engineering Practices

An Integrated Seismic Assessment Method for Urban Buildings and Roads

AbstractSeismic simulation of urban buildings and roads is significant for regional pre-disaster mitigation and post-disaster recovery. To consider the interrelated influences of buildings and roads, an integrated seismic assessment method for urban buildings and roads is proposed. The seismic damages of buildings were assessed using various methods based on structural characteristics and different degrees of available building information. Both physical and topological characteristics of the road network are considered in the proposed method to determine post-earthquake road network traffic capacity. To quantitatively evaluate post-earthquake road network traffic capacity, we comprehensively considered the seismic damage to roads, blockages caused by earthquake-induced debris, and the potential risk of falling debris from damaged buildings. The proposed integrated seismic assessment method was applied to a real earthquake event to demonstrate its feasibility and effectiveness, and also applied to a real city, of which information on buildings and roads was based on open-source data and statistical data, to demonstrate its applicability. The proposed method provides a solid prediction on the seismic performance of urban buildings and road networks, serving as a reference for urban earthquake disaster rescue and relief.

State of the interactomes: an evaluation of molecular networks for generating biological insights.

Advancements in genomic and proteomic technologies have powered the creation of large gene and protein networks ("interactomes") for understanding biological systems. However, the proliferation of interactomes complicates the selection of networks for specific applications. Here, we present a comprehensive evaluation of 45 current human interactomes, encompassing protein-protein interactions as well as gene regulatory, signaling, colocalization, and genetic interaction networks. Our analysis shows that large composite networks such as HumanNet, STRING, and FunCoup are most effective for identifying disease genes, while smaller networks such as DIP, Reactome, and SIGNOR demonstrate stronger performance in interaction prediction. Our study provides a benchmark for interactomes across diverse biological applications and clarifies factors that influence network performance. Furthermore, our evaluation pipeline paves the way for continued assessment of emerging and updated interaction networks in the future.

On the use of feed-forward neural networks in the context of surrogate aeroelastic simulations

AbstractFor a few decades now, the proliferation of digital computers has driven the development of increasingly complex models to study the physical phenomena that are part of our reality. Particularly, in the field of aeronautics and renewable energy (wind), correct aeroelastic modeling is crucial for many reasons: structural and aerodynamic optimization, determining operational envelopes, and avoiding destructive aeroelastic phenomena such as divergence or flutter, among others. Furthermore, the study of systems involving multiple fields of physics (aerodynamics, structural dynamics, control, etc.) is characterized by exhibiting highly nonlinear phenomena (limit cycle oscillations, bifurcations, chaos, etc.), which are very challenging to capture with linear approximations or simplified models. In this work, we present a comprehensive statistical analysis of the performance of shallow feed-forward neural networks (FNNs) to capture supercritical Hopf bifurcations when dealing with aeroelastic flutter. The FNNs are trained by considering data sets generated by using two different aeroelastic models of increasing complexity. For the structural model, we consider a two-degree-of-freedom model consisting of an airfoil oscillating in pitch and plunge. The aerodynamic forces are accounted for by using two different flow solvers: (1) a non-compressible two-dimensional linear (but ergodic) model based on Wagner’s theory (referred as Fung’s model), which results in analytical expressions for the lift and aerodynamic moment, and (2) a two-dimensional version of the well-known unsteady vortex-lattice method (UVLM). The assessment of the resulting FNN-based models is carried out through a Monte Carlo experiment over R replicates. As a measure of performance, we use the mean-squared error test associated with the estimators, here the system’s response and its consistent aerodynamic coefficients. We also discuss, in detail, the behavior of FNN-based surrogate aeroelastic frameworks when they are trained with data coming from Fung-based or UVLM-based aeroelastic simulations. Furthermore, we highlight a number of challenges faced by shallow FNNs, as well as some difficulties when integrated into surrogate aeroelastic environments. Finally, we provide explanations to questions raised throughout the article and conjecture some others without a definitive answer.

Local Pyramid Vision Transformer: Millimeter-Wave Radar Gesture Recognition Based on Transformer with Integrated Local and Global Awareness

A millimeter-wave radar is widely accepted by the public due to its low susceptibility to interference, such as changes in light, and the protection of personal privacy. With the development of the deep learning theory, the deep learning method has been dominant in the millimeter-wave radar field, which usually uses convolutional neural networks for feature extraction. In recent years, transformer networks have also been highly valued by researchers due to their parallel processing capabilities and long-distance dependency modeling capabilities. However, traditional convolutional neural networks (CNNs) and vision transformers each have their limitations: CNNs usually overlook the global features of images and vision transformers may neglect local image continuity, and both of them may impede gesture recognition performance. In addition, whether CNN or transformer, their implementation is hindered by the scarcity of public radar gesture datasets. To address these limitations, this paper proposes a new recognition method using a local pyramid visual transformer (LPVT) based on millimeter-wave radar. LPVT can capture global and local features in dynamic gesture spectrograms, ultimately improving the recognition ability of gestures. In this paper, we mainly carried out the following two tasks: building the corresponding datasets and executing gesture recognition. First, we constructed a gesture dataset for training. In this stage, we use a 77 GHz radar to collect the echo signals of gestures and preprocess them to build a dataset. Second, we propose the LPVT network specifically designed for gesture recognition tasks. By integrating local sensing into the globally focused transformer, we improve its capacity to capture both global and local features in dynamic gesture spectrograms. The experimental results using the dataset we constructed show that the proposed LPVT network achieved a gesture recognition accuracy of 92.2%, which exceeds the performance of other networks.

Optimizing System Performance with Adaptive Algorithms for Signal Processing in Wireless Communication Networks

In modern wireless communication networks, the need for high-speed, reliable, and efficient data transmission is paramount. Signal processing algorithms play a critical role in optimizing network performance, especially under dynamic conditions such as varying network loads and environmental interference. This paper presents an in-depth study of adaptive signal processing algorithms designed to improve the performance of wireless communication systems. We examine various techniques, including adaptive filtering, channel estimation, and interference cancellation, and assess their effectiveness in optimizing signal quality and network throughput. Through simulation and real-world case studies, the paper demonstrates how these algorithms can be applied to mitigate signal distortion and reduce data transmission errors in wireless networks. The results show that adaptive algorithms significantly enhance the overall system performance, particularly in environments with fluctuating signal conditions. The paper concludes with recommendations for further development in adaptive algorithms for future wireless communication standards, particularly 5G and beyond.