Wireless Network Optimization Research Articles

Свойства трафика видеоконференций 5G и его прогнозирование методами искусственного интеллекта

The objectives and prospects of short- and long-term forecasting of data traffic in 5G networks are outlined. An overview of current traffic forecasting methods is presented. A large number of works indicate a variety of ap-proaches to the analysis and forecasting of mobile traffic. These approaches include both traditional statistical methods and deep learning methods, which is an important factor for the development of network technologies and optimization of radio access networks. Forecasting and management plays an important role both at the level of the entire 5G network and at the level of its individual components. With the increase in the volume of big data, traffic forecasting becomes a difficult task due to mobility and different user behavior. In connection with this circumstance, using the example of mobile traffic data from Google Meet, MS Teams and Zoom video conferencing systems, the features of this type of traffic are analyzed and it is concluded that the distribution of time intervals between packet arrivals corresponds to a stable distribution. Based on the results of studying some machine learning methods designed to predict time series, a comparative analysis of the effectiveness of these methods for short-term forecasting of traffic intensity was performed. The following models were used for analysis and prediction: naive seasonal, exponential smoothing, linear regression, ARIMA, Theta and NBEATS. It was found that linear regression provides the best quality indicators for Google Meet traffic, the exponential smoothing method shows the best quality indicators for MS Teams, and the Theta method has the best indicators for Zoom.

Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges

Generative AI for the Optimization of Next-Generation Wireless Networks: Basics, State-of-the-Art, and Open Challenges

Wireless Sensor Network Optimization for Constrained Environments With Limited a Priori Target Information

Wireless Sensor Network Optimization for Constrained Environments With Limited a Priori Target Information

Research on a high‐performance signal distribution reconstruction algorithm for wireless communication networks

AbstractWith the rapid development of communication technology and the increasing demand for coverage refinement in wireless communication networks, the optimization of wireless communication networks is faced with unprecedented challenges. Obtaining the signal distribution map of wireless communication networks efficiently has become a popular area of study in this field. This paper considers a distributed sensing network architecture, a radial basis function neural network is used to process electromagnetic data and optimize the parameters of the random forest model. Then, interpolation processing of incomplete electromagnetic data is achieved by the improved random forest model, based on which a signal distribution map of the wireless communication network is reconstructed. The results indicate that the proposed algorithm yields high interpolation accuracy. The average error between the real signal distribution and the reconstructed signal distribution is 2.7973 dBm when the proportion of sampled nodes is 1%, and the similarity of the reconstructed signal distribution map to the original signal distribution map is good, demonstrating certain application prospects.

An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

An intelligent fault node diagnosis and classification approach using XGBoost with whale optimization in wireless sensor networks

AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization

AoI, Timely-Throughput, and Beyond: A Theory of Second-Order Wireless Network Optimization

An improved beetle antennae search algorithm and its application in coverage of wireless sensor networks

This manuscript presents ABSAS-CS-GSA, an improved iteration of the beetle antennae search (BAS) algorithm, tailored to address the shortcoming of the BAS with low convergence accuracy and easily falling into local optima. The improvements are structured into three principal components: first, dynamic step-size adjustment predicated on initial population to bolster solution accuracy and expedite convergence; second, position update mechanism integrating golden sine algorithm to diversify search patterns and expedite convergence; and third, population disturbance based on vertical and horizontal cross strategy to avoid falling into local optima. Experimental results on 12 benchmark functions demonstrate ABSAS-CS-GSA's superiority over the standard BAS and its four variants, as well as over two classic swarm intelligence algorithms. The simulation results of the algorithm applied to coverage optimization of wireless sensor networks with 30 nodes and 50 nodes reveal a marked improvement in both optimal and average coverage metrics relative to seven alternative algorithms. The refined algorithm exhibits excellent performance and is well-suited for tackling the coverage issue within wireless sensor networks.

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

EDRP-GTDQN: An adaptive routing protocol for energy and delay optimization in wireless sensor networks using game theory and deep reinforcement learning

Performance Optimization and Link Reliability in Wireless Body Area Networks

Long-lasting connectivity and energy-efficient systems are needed for wireless body area networks (WBANs). In addition to the growing commercialization of WBANs, health monitoring applications demand improved quality of service (QoS). For WBAN performance characteristics to improve, it is essential to develop a dependable and energy-efficient link. We provide a cross-layer routing strategy for improving WBAN quality of service in this study. This method employs a cost function that linearly combines the individual absorption rate functions, node energy ratio, and link dependability. This research investigates how the performance of the network varies depending on the parameter combinations used and the size of the contention window, and we use parametric modelling of the cost function. While the development of the QoS focuses on enhancing the packet delivery success rate and network throughput for applications of WBANs, the suggested algorithm primarily increases network lifetime durability by decreasing the node energy consumption with acceptable throughput. WBAN performance optimization criteria using advanced particle swarm optimization (APSO) are proposed in this research to emphasize increasing energy economy, decreasing end-to-end delay and increasing network throughput in various existing methods. The number of live nodes for the proposed method is higher than those of the PSO-LSMR, M-ATTEMPT and EERP. The value of First Node Died is 6301. The value of residual energy, 34.7 J, is also higher for the proposed method than for the compared state-of-the-art algorithms.

Offline Reinforcement Learning for Wireless Network Optimization With Mixture Datasets

Offline Reinforcement Learning for Wireless Network Optimization With Mixture Datasets

Selecting Outdoor Wireless Solution for Bara Prima Borneo Using Analytic Hierarchy Process

The advancement of wireless communication technology is critical for improving the efficiency, safety, and productivity of mining operations. This study focuses on evaluating and recommending an optimal outdoor wireless network solution for Bara Prime Borneo (BPB) mining operations in East Kalimantan, Indonesia, using the Analytic Hierarchy Process (AHP) for decision-making. The current Wi-Fi infrastructure faces significant challenges, including limited coverage, interference, and scalability issues, which hinder its effectiveness in the demanding mining environment. To address these challenges, the study employs a comprehensive approach to identify stakeholder expectations and value perceptions, explore alternative wireless network designs such as Private LTE and Kinetic Mesh, and systematically select the most suitable solution using AHP. Through discussions with subject matter experts and secondary data collection, the study outlines the strengths, weaknesses, opportunities, and threats (SWOT) associated with the existing Wi-Fi network and potential alternatives. Using AHP, the study prioritizes various criteria such as coverage, reliability, cost, scalability, and security. The recommended design aims to bridge the gap between current capabilities and future needs, ensuring robust and extensive network coverage that supports various digital applications essential for modern mining. By selecting the proposed solution using AHP, BPB can achieve a more reliable and scalable wireless network, enhancing overall operational efficiency and safety while meeting the evolving demands of its mining operations.

Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

Deployment optimization in wireless sensor networks using advanced artificial bee colony algorithm

Energy efficient clustering and routing protocol based on quantum particle swarm optimization and fuzzy logic for wireless sensor networks

Clustering and routing protocols play a pivotal role in reducing energy consumption and extending the lifespan of wireless sensor networks. However, optimizing energy efficiency to maximize network longevity remains a primary challenge for these protocols. This paper introduces QPSOFL, a clustering and routing protocol that integrates quantum particle swarm optimization and a fuzzy logic system to enhance energy efficiency and prolong network lifespan. QPSOFL employs an enhanced quantum particle swarm optimization algorithm to select optimal cluster heads, utilizing Sobol sequences for population diversification during initialization. Additionally, it incorporates Lévy flight and Gaussian perturbation-based position updates to prevent trapping in local optima. Benchmark experiments validate QPSOFL's efficacy compared to Harris Hawks Optimization (HHO), Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), and Quantum Particle Swarm Optimization (QPSO), focusing on accuracy, search capability, and convergence speed. Within QPSOFL, a fuzzy logic system determines the best next-hop cluster head based on descriptors such as residual energy, energy deviation, and relay distance. Extensive simulations compare QPSOFL's performance in terms of network lifetime, throughput, energy consumption, and scalability against existing protocols E-FUCA, IHHO-F, F-GWO, and FLPSOC, demonstrating its superior performance over these counterparts.

Enhancing Reptile search algorithm with shifted distribution estimation strategy for coverage optimization in wireless sensor networks

Enhancing Reptile search algorithm with shifted distribution estimation strategy for coverage optimization in wireless sensor networks

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

Federated Split Learning With Joint Personalization-Generalization for Inference-Stage Optimization in Wireless Edge Networks

Federated Split Learning With Joint Personalization-Generalization for Inference-Stage Optimization in Wireless Edge Networks

Optimizing Wireless Networks Performance Through Adaptive Machine Learning Strategies

This research aims to enhance wireless network performance through the development and implementation of a novel machine learning-based algorithm. The methodology involves comprehensive data collection in the Uttarakhand region, specifically in Dehradun, Haridwar, and Rishikesh, through LTE network drive tests. The collected data, over six months, serves as the foundation for the proposed algorithm. The research design focuses on parameterizing essential factors of path loss to optimize coverage. Various drive test tools, including NEMO, Agilent, TEMS, and XCAL-Mobile, are employed to measure and analyze field Received Signal Strength Indicators (RSSI) data. Statistical tools are utilized to collect network parameters, assess coverage, and analyze real-time mobile network data. The research objectives include the development and evaluation of the machine learning algorithm, comparing its performance across diverse network scenarios, and benchmarking against existing state- of-the-art algorithms. The proposed methodology provides a structured and relevant framework to achieve these objectives, fostering advancements in wireless network optimization through innovative machine-learning approaches.

Coverage optimization of wireless sensor networks based on bottle sea sheath optimization algorithm

To tackle the problem of inadequate placement of wireless sensor networks’ sensor nodes, a proposed algorithm aims to enhance network coverage through a more optimal approach. Recognizing limitations in the Salp Swarm Algorithm (SSA) optimization, specifically its insufficient local search capability and susceptibility to local extreme values, a hybrid strategy is introduced in the search step. This strategy involves adaptive weight factor adjustments and incorporates Levy flight, achieving a better balance between global search, convergence speed, and computational accuracy. Consequently, this refinement contributes to improved effective network coverage, enhancing the overall network coverage in wireless sensor networks and facilitating faster coverage of the nodes.

Energy optimization for wireless sensor network using minimum redundancy maximum relevance feature selection and classification techniques

In wireless sensor networks (WSN), conserving energy is usually a basic issue, and several approaches are applied to optimize energy consumption. In this article, we adopt feature selection approaches by using minimum redundancy maximum relevance (MRMR) as a feature selection technique to minimize the number of sensors thereby conserving energy. MRMR ranks the sensors according to their significance. The selected features are then classified by different types of classifiers; SVM with linear kernel classifier, naïve Bayes classifier, and k-nearest neighbors classifier (KNN) to compare accuracy values. The simulation results illustrated an improvement in the lifetime extension factor of sensors and showed that the KNN classifier gives better results than the naïve Bayes and SVM classifier.

Decentralized Machine Learning for Dynamic Resource Optimization in Wireless Networks using Reinforcement Learning

Efficient allocation of resources is crucial for optimizing wireless networks that face constraints in bandwidth, power, and spectrum. This paper proposes a decentralized reinforcement learning (RL) model that departs from traditional centralized paradigms to revolutionize resource optimization. The proposed model empowers individual wireless devices with autonomous decision-making capabilities, enhancing adaptability and scalability by leveraging Deep Q-Network (DQN) and Proximal Policy Optimization (PPO). The innovative integration of memory mechanisms facilitates learning from past experiences, addressing the dynamic nature of wireless environments. This decentralized RL model offers practical implications for improved efficiency, adaptability, and reliability in wireless network resource optimization. By transforming individual devices into collaborative decision-makers, our proposed model contributes to a resilient and responsive wireless communication infrastructure. The specific contributions of this paper include the pioneering use of DQN and PPO algorithms within a multi-agent system, offering a groundbreaking solution for dynamic resource optimization in wireless networks.