Wireless Networks Research Articles

Enhancing Martial Arts Safety Through 6G-Enabled Risk Assessment and Alert Systems

This paper proposes an advanced approach to enhance athlete safety in martial arts competitions by integrating 6G-enabled technologies with enterprise engineering and semantic integrity principles. Wearable sensors within a 6G network capture vital physiological data, such as temperature, pulse rate, opponent-induced force, and ECG signals. These data are analyzed using an Optimized Support Vector Risk Prediction Model (OSVRPM), optimized through the Vinegar Fly Optimization Algorithm (VFOA), to accurately assess athlete risk levels during competitions. Real-time alert messages, facilitated by the 6G wireless network, are disseminated to medical personnel, judges, referees, and athletes upon identifying heightened risk, ensuring swift intervention and reinforcing safety protocols in martial arts tournaments. This innovative integration exemplifies the application of modeling with enterprise engineering and semantic integrity to safeguard athletes in dynamic competitive environments.

Sustainable Resource Allocation and Base Station Optimization Using Hybrid Deep Learning Models in 6G Wireless Networks

Researchers are currently exploring the anticipated sixth-generation (6G) wireless communication network, poised to deliver minimal latency, reduced power consumption, extensive coverage, high-level security, cost-effectiveness, and sustainability. Quality of Service (QoS) improvements can be attained through effective resource management facilitated by Artificial Intelligence (AI) and Machine Learning (ML) techniques. This paper proposes two models for enhancing QoS through efficient and sustainable resource allocation and optimization of base stations. The first model, a Hybrid Quantum Deep Learning approach, incorporates Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). CNNs handle resource allocation, network reconfiguration, and slice aggregation tasks, while RNNs are employed for functions like load balancing and error detection. The second model introduces a novel neural network named the Base Station Optimizer net. This network includes various parameters as input and output information about the condition of the base station within the network. Node coverage, number of users, node count and user locations, operating frequency, etc., are different parametric inputs considered for evaluation, providing a binary decision (ON or SLEEP) for each base station. A dynamic allocation strategy aims for network lifetime maximization, ensuring sustainable operations and power consumption are minimized across the network by 2 dB. The QoS performance of the Hybrid Quantum Deep Learning model is evaluated for many devices based on slice characteristics and congestion scenarios to attain an impressive overall accuracy of 98%.

Mobility and Temperature Sensitive Energy Efficient Routing (MTS-EER) protocol in a wireless body area network

Sensor technology advancements have provided the platform to implement wireless body area networks, thanks to the nanosized sensor units capable of sensing, aggregating, and forwarding physiological information. The collected information is routed to the desired destination unit for data analysis and decision-making in remote healthcare. However, improving energy utilization remains a brain-teasing problem for the research community, especially considering imbalanced energy consumption and postural movements. Mobility contributes to disconnectivity issues, high energy drainage, and retransmission delays. In addition, the sensor node’s thermal level also poses a challenge in maintaining safer and reliable data transmission. To overcome these issues, a novel Mobility and Temperature Sensitive Energy-Efficient Routing ¬(MTS-EER) algorithm has been proposed that includes a two-step process. In step 1, an Intelligent Path Estimation Function (IPEF) is designed considering the sensor’s mobility, temperature, and energy level. IPEF depends on crucial parameters i.e.: - residual energy, signal-to-noise ratio (SNR), distance, total energy, and most importantly temperature of the sensor unit. The sensor node with the least IPEF is selected as the Cluster Head (CH). In step 2, an optimized and sustainable energy conservation model (OSECM) is implemented based on the Adaptive Transmission Power (ATP) and the Power Management Module (PMM). The ATP conserves the energy via intelligently varying the transmission power and PMM manages the sleep pattern of sensor nodes to yield a high network lifetime and efficient energy utilization. The algorithm includes a clustering approach with dual sink nodes to conserve energy and improve reliability. Finally, the results are compared with the recent state-of-the-art research work. The proposed algorithm provides better results considering residual energy, throughput, network lifetime, and end-to-end delay.

Research on Online Vocal Music Smart Classroom-Assisted Teaching Based on Wireless Network Combined With Artificial Intelligence

With the rapid advancement of information technology, online education based on big data and artificial intelligence is a hot research topic in education. This study focuses on applying big data and AI in online vocal wisdom classes to enhance personalized teaching and effectiveness. It aims to address issues in traditional vocal education like lack of personalized guidance and poor outcomes. The research proposes an intelligent learning system for vocal education, featuring modules for data collection, analysis, and intelligent recommendations. Functions include personalized learning paths, teaching assistance, real-time feedback, and assessment. Experiments show significant improvements in student outcomes and satisfaction. This innovative approach contributes to enhancing vocal education through personalized and intelligent teaching, offering valuable insights for future education development. Online vocal wisdom classrooms leveraging big data and AI represent a crucial direction with broad application prospects.

Ground-to-UAV sub-terahertz channel measurement and modeling

Unmanned aerial vehicle (UAV) assisted wireless communications have been expected to play a vital role in the next generation of wireless networks. UAVs can serve as either repeaters or data collectors within the communication link, thereby potentially augmenting the efficacy of communication systems. Despite their promise, the channel analysis and modeling specific to terahertz (THz) wireless channels leveraging UAVs remain under-explored. This work delves into a ground-to-UAV channel at 140 GHz, with a specific focus on the influence of UAV hovering behavior on channel performance. Employing experimental measurements through an unmodulated channel setup and a geometry-based stochastic model (GBSM) that integrates three-dimensional positional coordinates and beamwidth, this work evaluates the impact of UAV dynamic movements and antenna orientation on channel performance. Our findings highlight the minimal impact of UAV orientation adjustments on channel performance and underscore the diminishing necessity for precise alignment between UAVs and ground stations as beamwidth increases.

An energy-efficient JT-CoMP enabled framework with adaptive OMA/NOMA in HetNets

The increasing demand for higher data rates and improved energy efficiency (EE) in next-generation wireless networks necessitates the optimized selection of multiple-access and coordination techniques. A hybrid joint transmission (JT)-coordinated multi-point (CoMP) enabled orthogonal multiple access (OMA)/non-orthogonal multiple access (NOMA) technique, combining the spectral efficiency (SE) and capacity benefits of CoMP NOMA with the interference mitigation of CoMP OMA, offers a highly adaptable solution for future wireless networks. This paper studies the joint optimization of CoMP/non-CoMP selection, OMA/NOMA selection, power allocation, and user pairing, with the objective of maximizing the EE in the network. A Dynamic CoMP user selection with energy-efficient adaptive multiple access (DCEAMA) algorithm to solve the formulated problem is proposed. Our Monte Carlo simulations show that the DCEAMA surpasses both the pure CoMP OMA and CoMP NOMA schemes in terms of EE, with an average increase of 38% and 26% respectively. We compare our heuristic technique to an exhaustive search strategy to evaluate its efficiency. The findings indicate that our strategy produces comparable EE across various power levels with reduced computational complexity.

Artificial Intelligence System Based on Wireless Network and Optical Sensor Recognition Application in Museum Interactive VR Design

Artificial Intelligence System Based on Wireless Network and Optical Sensor Recognition Application in Museum Interactive VR Design

Wireless network topology optimization algorithm based on discrete variables

In response to the missing discrete variables in current wireless network topology optimization, an improved particle swarm optimization algorithm based on fusion of discrete variables is proposed to obtain network discrete variables. A wireless network topology optimization model is constructed. The research results indicate that it has better anti-interference performance in complex situations, which contributes to improving network load balancing. The topology obtained by this method has independence and predictability. When optimizing network topology, it has high network node coverage. When the network nodes are 50, 100, 150, and 200, the connectivity is 99.85 %, 93.64 %, 91.25 %, and 90.18 %, respectively. The testing time is 19s, 34s, 54s, and 64s respectively, which has the best optimization performance. The method can effectively improve the missing discrete variables in wireless network topology optimization, which has good performance.

Light Image Enhancement Application in Visual Communication Art Based on Wireless Network Sensing and Image Segmentation Method

Light Image Enhancement Application in Visual Communication Art Based on Wireless Network Sensing and Image Segmentation Method

Enhancing Wireless Network Efficiency with the Techniques of Dynamic Distributed Load Balancing: A Distance-Based Approach.

The unique combination of the high data rates, ultra-low latency, and massive machine communication capability of 5G networks has facilitated the development of a diverse range of applications distinguished by varying connectivity needs. This has led to a surge in data traffic, driven by the ever-increasing number of connected devices, which poses challenges to the load distribution among the network cells and minimizes the wireless network performance. In this context, maintaining network balance during congestion periods necessitates effective interaction between various network components. This study emphasizes the crucial role that mobility management plays in mitigating the uneven load distribution across cells. This distribution is a significant factor impacting network performance, and effectively managing it is essential for ensuring optimal network performance in 5G and future networks. The study investigated the complexities associated with congested cells in wireless networks to address this challenge. It proposes a Dynamic Distance-based Load-Balancing (DDLB) algorithm designed to facilitate efficient traffic distribution among contiguous cells and utilize available resources more efficiently. The algorithm reacts with congested cells and redistributes traffic to its neighboring cells based on specific network conditions. As a result, it alleviates congestion and enhances overall network performance. The results demonstrate that the DDLB algorithm significantly improves key metrics, including load distribution and rates of handover and radio link failure, handover ping-pong, and failed attached requests.

Enhancing security for smart healthcare in wireless body area networks using a novel adversarial detection using ACR BiLSTM with multi-batch stochastic gradient descent

Enhancing security for smart healthcare in wireless body area networks using a novel adversarial detection using ACR BiLSTM with multi-batch stochastic gradient descent

Optimized Adaptive Multi-Scale Dual An for Multi-Objective CHS and Energy-Aware Routing in 6G WC

In this research work, an Adaptive Multi-Scale Dual Attention Network with ZOA for Multi-Objective CHS with energy-aware routing in 6G wireless Communication (CHS-EAR-AM-SDAN-6G) is proposed to secure the data transmission by selecting optimum cluster heads in the 6G Wireless Communication network. Initially, the nodes are gathered together to form a cluster using an Adaptive Multi-Scale Dual Attention Network (AM-SDAN). The Zebra Optimization Algorithm (ZOA) strategically selects Cluster Heads (CHs) in wireless networks based on a multi-objective fitness function (MoFF) that minimizes energy consumption while considering factors, like distance, delay, and traffic density. The path and minimum value of fitness are recognized as the routing path and statistics are promoted to the sink node through the cluster head. The proposed scheme has been applied in Python and productivity of the proposed method is predictable with the help of several performances they are energy consumption, detection rate, computational time, packet delivery rate, number of alive nodes, and security. The performance of the proposed CHS-EAR-AMSDAN-6G method attains 25.93%, 24.81%, and 23.38% of alive nodes, 24.45%, 26.71% and 21.32% lower packet delivery rate, 27.56%, 26.43%, and 28.61% low computational period, related with three current methods, such as Generative Adversarial Learning for ITM in 6G Wireless Communication Networks (CHS-EAR-GAL-ITM-6G), ML Algorithms for the Future 6G WCN (CHS-EAR-ML-6G), Energy Efficient Distributed Federated Learning to the 6G Wireless Communication Networks (CHS-EAR-EEDFL-6G), respectively.

Wideband circularly polarized reconfigurable metasurface antenna for 5G applications

Abstract A framework of a metasurface (MTS) – based wideband circularly polarized (CP) reconfigurable antenna for fifth generation (5G) wireless systems operating in the sub-6 GHz mid-frequency range is presented in this article. The proposed structure contains a simple patch antenna, which serves as the primary source, a shorting pin, two ring slots, and a metasurface superstrate. The shorting pin and two ring slots produce perturbation resulting in circular polarization with a narrow axial ratio (AR) bandwidth. A metasurface (MTS) superstrate composed of 4 × 4 rectangular patches stacked over the primary source antenna generates additional resonances that significantly improve the −10 dB impedance and 3 dB axial ratio (AR) bandwidth. Two PIN diodes are employed to alter the circular polarization between left-hand circular polarization (LHCP) and right-hand circular polarization (RHCP). The prototype is made using an FR-4 epoxy substrate and has an overall size of 50 mm × 35 mm × 4.2 mm. The antenna has a −10 dB bandwidth of 44 % (4.0–6.2) and a 3 dB axial ratio bandwidth of 28 % (4.2–5.6). It also has a 3 dB beam width of 74° and 85° in the E and H planes, a gain of 7.1 dBi, cross-polar isolation of more than 15 dB, and a unidirectional radiation pattern. The simulated and measured results confirm that the implemented antenna is promising for sub-6 GHz 5G communication systems, particularly cognitive radio, wireless body area networks (WBAN), and satellite communication systems.

Analyzing ASEP with imperfect‐phase error and various modulation schemes on IRS‐aided wireless network over κ–μ fading channel

AbstractNext‐generation wireless systems constitute a promising technology, and intelligent reflecting surface (IRS)‐aided mobile communications can be used to design and simulate a smart and flexible radio environment. We examine the average symbol error probability (ASEP) on an IRS‐aided wireless network over a κ–μ fading channel with imperfect phase error. In addition, the ASEP for non‐coherent M‐ary frequency shift keying, M‐ary phase shift keying, and M‐ary differential phase shift keying on an IRS‐aided wireless network over a κ–μ fading channel are analyzed. Furthermore, exact closed‐form mathematical expressions for ASEP across different modulation schemes including ASEP with imperfect phase error are derived. The accuracy of the derived expressions is validated through simulations. The system effectiveness is comprehensively examined, uncovering valuable insights into channel fading values and the number of reflecting components.

A low‐profile, wideband, circularly polarized, slotted, U‐shaped textile antenna for W‐BAN applications

AbstractWe propose a compact, wideband, U‐shaped, circularly polarized textile antenna (UCPTA) resonating at 2.45 GHz for wireless body area network (W‐BAN) applications. The radiator of the antenna is printed on the top of the substrate (size of mm3; ) and has a full ground plane on the other side. The proposed antenna's substrate consists of a single layer of denim (), and conductive copper tape was used to fabricate the patch. The antenna has a low‐profile design that is suitable for wearable applications. A small slot was introduced at the center of the U‐shaped patch to enhance the 3 dB axial ratio bandwidth (ARBW) up to 55.17%. The proposed antenna radiates with a 10 dB return loss (RL), impedance bandwidth (IBW) of 64.40%, and a simulated gain of 5.1 dBi. The wearable textile antenna with broadside radiation response is appropriate for off‐body communications. The prototype of UCPTA is fabricated for experimental validation. The measured results for the fabricated antenna were in good agreement with the simulated results. Further, it has been demonstrated that the antenna's performance is stable during structural deformation and human body loading. This textile antenna covers an ARBW in the frequency band of 2.1 GHz–3.72 GHz and has a broad IBW (2 GHz–3.9 GHz) for LTE (2.17 GHz), MBAN (2.360 GHz–2.4 GHz), WPAN (2.395 GHz–2.4 GHz), and WLAN (2.4 GHz–2.482 GHz) applications.

Retraction Note: CMDP-based intelligent transmission for wireless body area network in remote health monitoring

Retraction Note: CMDP-based intelligent transmission for wireless body area network in remote health monitoring

An innovative method for improving the performance of unmanned aerial vehicle‐based generalized spatial modulation and non‐orthogonal multiple access in 6G networks

AbstractThe rapid evolution of wireless networks is leading to the emergence of 6G, promising unprecedented performance. We propose a nonorthogonal multiple access (NOMA)‐based multiuser generalized spatial modulation (GSM) system that allows various transmitting antennas. Integrating unmanned aerial vehicles (UAVs) with NOMA and GSM enhances overall network performance. This paper proposes optimizing UAV‐GSM‐NOMA systems in 6G using the butterfly optimization algorithm (BOA) in the two‐wave with diffuse power (TWDP) channel. Results are compared with those obtained from Nakagami and Rayleigh channels. Objectives include enhancing the sumrate, outage probability (OP), and energy efficiency (EE) in perfect and imperfect channel state information (CSI). Simulation results yield significant improvements in the TWDP channel compared with the Nakagami and Rayleigh channels. The OP is reduced, the sumrate is enhanced, and the EE is notably improved. These findings underscore the BOA's potential for 6G UAV‐GSM‐NOMA networks in challenging channel environments, ensuring reliable and efficient communication.

Image Tracking and Segmentation Algorithms Based on Laser Sensors and Wireless Network Devices in Sports Target Detection

Image Tracking and Segmentation Algorithms Based on Laser Sensors and Wireless Network Devices in Sports Target Detection

Ultra-sensitivity in reconstructed exceptional systems

ABSTRACT Sensors are of fundamental importance and widely used in modern society, such as in industry and environmental monitoring, biomedical sample ingredient analysis and wireless networks. Although numerous sensors have been developed, there is a continuous demand for sensors with increased sensitivity, to detect signals that were previously undetectable. Recently, non-Hermitian degeneracies, also known as exceptional points (EPs), have attracted attention as a way of improving the responsiveness of sensors. In contrast to previous investigations, here we present a new approach to achieving ultra-sensitivity by reconstructing exceptional systems. In the reconstruction process, some eigenstates near the previous EPs are utilized, and non-reciprocal long-range couplings are introduced. The sensitivities of our reconstructed systems have improved by several orders of magnitude compared to those based on EPs. Furthermore, we design and fabricate corresponding integrated circuit sensors to demonstrate the scheme. Our work paves the way for the development of highly sensitive sensors, which have a wide range of applications in various fields.

Efficient resource allocation using whale optimization for cell-free massive MIMO networks in 6G HetNet

The increase in data rate demands has driven the development of Cell-Free massive Multiple-Input Multiple-Output (CFmMIMO) technology in 6G Heterogeneous Networks (HetNets). This paper proposes an integration of Unmanned Aerial Vehicles (UAVs) and Device-to-Device (D2D) communication in CFmMIMO, aiming to enhance network capacity, coverage, and service delivery in next-generation wireless networks. The paper investigates uplink transmission in a network where Access Points (APs) have imperfect channel state information. It considers Cell-free User Equipment (CUE), UAVs, and D2D pairs, deriving closed-form uplink achievable rates for each user type. Moreover, two optimization problems are formulated to enhance user data rates: one focuses on maximizing the sum data rate through pilot assignment, while the other aims to achieve weighted max–min power control through power allocation. Furthermore, to address these problem, we propose a Modified Whale Optimization Algorithm (MWOA) and a novel Pilot Assignment based on Whale Optimization Algorithm (PAWOA). Performance studies demonstrate significant improvements over state-of-the-art algorithms.