Wireless Communication Infrastructure Research Articles

Behind the Door: Practical Parameterization of Propagation Parameters for IEEE 802.11ad Use Cases

The integration of the 60 GHz band into the IEEE 802.11 standard has revolutionized indoor wireless services. However, this band presents unique challenges to indoor wireless communication infrastructure, originally designed to handle data traffic in residential and office environments. Estimating 60 GHz signal propagation in indoor settings is particularly complicated due to dynamic contextual factors, making it essential to ensure adequate coverage for all connected devices. Consequently, empirical channel modeling plays a pivotal role in understanding real-world behavior, which is characterized by a complex interplay of stationary and mobile elements. Given the highly directional nature of 60 GHz propagation, this study addresses a seemingly simple but important question: what is the impact of employing highly directive antennas when deviating from the line of sight? To address this question, we conducted an empirical measurement campaign of wireless channels within an office environment. Our assessment focused on power losses and distribution within an angular range while an indoor base station served indoor users, simulating the operation of an IEEE 802.11ad high-speed WLAN at 60 GHz. Additionally, we explored scenarios with and without pedestrian movement in the vicinity of wireless terminals. Our observations reveal the presence of significant antenna lobes even in obstructed links, indicating potential opportunities to use angular combiners or beamformers to enhance link availability and the data rate. This empirical study provides valuable information and channel parameters to simulate 60 GHz millimeter wave (mm-wave) links in indoor environments, paving the way for more efficient and robust wireless communication systems.

Strategic Deployment of Ducklink Wireless Devices for Disaster Mitigation and Management in Nueva ECIJA University of Science and Technology Sumacab Campus

A significant challenge during disasters is the breakdown of traditional communication systems, impeding rescue operations and information dissemination. To tackle this issue, researchers conducted a study on strategically deploying DuckLink wireless devices within a small community, particularly at NEUST Sumacab Campus. Utilizing tools like Google Maps, Google Earth, and WiFi Heat Map, optimal locations for device placement were identified. Analyzing signal range and performance characteristics, the study endeavored to enhance disaster management capabilities on campus. Findings revealed DuckLink devices operate with omnidirectional polarity, ensuring reliable communication, with a clear correlation between distance and message transmission time emphasizing strategic device placement for efficient campus-wide connectivity. Recommending the installation of 112 devices across 79 buildings, with key locations designated for Papaduck receivers and Mamaduck transmitters, this research offers crucial insights into disaster management strategies, optimizing wireless communication infrastructure in educational environments, thus contributing to safer and more resilient communities.

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.

Dynamic Neural Network-Based Resource Allocation Framework for Latency-Aware and Energy-Efficient Optimization in Wireless Communication Systems

In wireless communication, efficiency is a crucial factor due to the inherent constraints of the radio frequency spectrum and the escalating demand for connectivity. With a limited spectrum available, efficient communication protocols are vital to maximize its utilization and accommodate the increasing number of devices. This study introduces a dynamic resource allocation system for wireless communication, encompassing essential components such as Base Stations (BS), Mobile Devices (MD), and a Centralized Controller (CC). BSs, serving as access points, establish wireless communication with MD within predetermined coverage areas. The CC acts as the neural centre of the system, collecting real-time data from both BSs and MDs to facilitate an adaptive resource allocation process. The comprehensive mechanism involves a scheduling algorithm for optimized data transmission, considering priority and Quality of Service (QoS) requirements, alongside power control mechanisms that dynamically adjust power levels to optimize energy efficiency while maintaining communication quality. MDs communicate with nearby BSs, relaying data transmission information to the CC, which strategically coordinates resources based on the collected data. This integrated system enhances resource utilization, minimizes latency, and improves energy efficiency, collectively contributing to the robustness and reliability of the wireless communication infrastructure.

Modelling of electromagnetic wave propagation at free space-human skin interface

The proliferation of the ambient environment with many wireless communication infrastructures has constituted a source of concern, at least from health point of view. Hence, there is a need for the investigation of electromagnetic wave interaction with human tissue. This paper models electromagnetic wave propagation at the free-space-human tissue interface, in an attempt to understand how electromagnetic waves interact with the human skin. Maxwell’s equations were used to derive the governing equations of propagating electromagnetic waves in free space while the solution of the boundary-value problem arising from the incident electromagnetic wave at the free space-human tissue interface was found through the use of appropriate boundary conditions. The solution led to the emergence of four Fresnel equations, a pair each for perpendicular and parallel polarizations of the incident wave, at the boundary. Through the use of the Fresnel equations obtained, components of the incident electromagnetic wave power at the boundary can be quantified. Typical profiles of the normalized reflected and absorbed powers at the free space-human skin interface at 28, 60, and 73 GHz were computed, using measured values of permittivity of human tissue. It is found that the total absorption of the power of the incident wave by the human skin occurs when the angle of incidence is between 690 and 770 for the three frequencies used. This is irrespective of the polarization of the incident wave. Information on such distinct angles of incidence for total absorption of power does not appear to exist in the literature as they represent new, previously unreported facts in the existing body of research. Furthermore, computed profiles of reflected power versus incidence angle displayed here, for perpendicular polarization, demonstrate strong alignments with established literature, thus reinforcing the credibility of the results presented in this paper.

Reduced Complexity Sequential Digital Predistortion Technique for 5G Applications

Wireless communication infrastructure is a key enabling technology for smart cities. This paper investigates a novel technique to enhance the performance of 5G base stations by addressing the compensation of nonlinear distortions caused by radiofrequency power amplifiers. For this purpose, a sequential digital predistortion approach that uses twin nonlinear two-box structure along with reduced sampling rates in the feedback path is proposed to implement a linearization system. Such a system is shown to have a correction bandwidth that exceeds the bandwidth of the feedback path. This is achieved by synthesizing the predistortion function in two successive characterization iterations. Both characterizations use the same hardware, which has a reduced sampling rate in the feedback path. Hence, the proposed predistorter scheme does not require any additional hardware compared to standard schemes. Moreover, coarse delay alignment is performed while identifying the memory polynomial function in order to further reduce the computational complexity of the proposed system. Experimental results using an inverse Class-F power amplifier demonstrate the ability of the proposed predistorter to achieve a correction bandwidth of 100 MHz with a feedback sampling rate as low as 25 MSa/s.

ANN-Based Intelligent Secure Routing Protocol in Vehicular Ad Hoc Networks (VANETs) Using Enhanced AODV

A vehicular ad hoc network (VANET) is a sophisticated wireless communication infrastructure incorporating centralized and decentralized control mechanisms, orchestrating seamless data exchange among vehicles. This intricate communication system relies on the advanced capabilities of 5G connectivity, employing specialized topological arrangements to enhance data packet transmission. These vehicles communicate amongst themselves and establish connections with roadside units (RSUs). In the dynamic landscape of vehicular communication, disruptions, especially in scenarios involving high-speed vehicles, pose challenges. A notable concern is the emergence of black hole attacks, where a vehicle acts maliciously, obstructing the forwarding of data packets to subsequent vehicles, thereby compromising the secure dissemination of content within the VANET. We present an intelligent cluster-based routing protocol to mitigate these challenges in VANET routing. The system operates through two pivotal phases: first, utilizing an artificial neural network (ANN) model to detect malicious nodes, and second, establishing clusters via enhanced clustering algorithms with appointed cluster heads (CH) for each cluster. Subsequently, an optimal path for data transmission is predicted, aiming to minimize packet transmission delays. Our approach integrates a modified ad hoc on-demand distance vector (AODV) protocol for on-demand route discovery and optimal path selection, enhancing request and reply (RREQ and RREP) protocols. Evaluation of routing performance involves the BHT dataset, leveraging the ANN classifier to compute accuracy, precision, recall, F1 score, and loss. The NS-2.33 simulator facilitates the assessment of end-to-end delay, network throughput, and hop count during the path prediction phase. Remarkably, our methodology achieves 98.97% accuracy in detecting black hole attacks through the ANN classification model, outperforming existing techniques across various network routing parameters.

Simulative Analysis of Column Mobility Model for Proactive and Reactive Routing Protocols in Highly Dense MANET

Abstract: One of the most promising fields of research in recent years is Mobile Ad Hoc Networks (MANET). The well-known advantages of the internet for specific types of applications lead to the fact that it is a wireless ad-hoc network. As a result, such networks can be utilized in circumstances where no other wireless communication infrastructure is present. A MANET is a network of wireless devices without any centralized control. A device can directly communicate with other devices using a wireless connection. For nodes that are located far from other nodes, multi-hop routing is employed. The functionality of route-finding is performed by routing protocols. The mobility model creates the movement pattern for nodes. This article discusses early research to address concerns about performance indicators for MANET routing protocols under the Column Mobility Model (CMM). Moreover, we discuss concerns regarding the designs of the related work, followed by the designed CMM model on the behavior of routing protocols.

A Novel Routing Control Method Using Federated Learning in Large-Scale Wireless Mesh Networks

Currently, the volume of communication by mobile terminals are increasing owing to 5G and other technologies. A robust network and appropriate routing control methods are requied to transmit information in unstable wireless communication environments and avoid congestion. Therefore, in recent years, numerous studies have been conducted on wireless mesh networks (WMNs), which provide a fault-tolerant communication environment by securing multiple communication paths and whose topology can be freely configured and extended. Additionally, machine learning routing is attracting attention as a new routing method for wireless communication environments. However, when performing machine learning on a large WMN, the learning time increases and rapid routing control may be impossible. In this study, we apply federated learning to machine learning and propose a machine-learning-based routing method that can be applied to large-scale WMNs. Furthermore, experimental results demonstrate the effectiveness of the proposed method in various environments: congestion avoidance is achieved in a large-scale WMN by machine-learning routing using federated learning. This study is expected to serve as a basis for significant progress in the realization of large-scale WMNs as wireless communication infrastructure.

Relative Localization of Wireless Sensor Nodes by Using the RSSI and ToA based distance estimations

Wireless sensor network is a popular area for both academic research and commercial applications. A wireless sensor network is made up of several sensor nodes connected over various wireless infrastructures such as Wi-Fi, Bluetooth, ZigBee, or WiMAX. Determining the relative or absolute position of the sensor nodes is essential information for many applications. In this work, we present a novel method for estimating the position of sensor nodes using the Received Signal Strength Indicator and Time of Arrival metrics of the preferred wireless communication infrastructure. Localization results based on both of the metrics and comparison of them with respect to distance between the nodes are presented, and a novel combined method using both the RSSI and ToA based distance estimations is presented. The proposed method estimates the position of the WSN using both methods, but the result of a single method is preferred depending on the distance between the nodes since within the first 5 m. The RSSI based method is superior to ToA and for farther distances ToA outperforms RSSI. The measurement results show that the combined method reduces the estimation error and performs better than both methods it is based on.

Survey: Self-Empowered Wireless Sensor Networks Security Taxonomy, Challenges, and Future Research Directions

In the recent past, patient wearable devices and implantable biosensors revealed exponential growth in digital healthcare, because they have the capability to allow access to the information anywhere and every time to improve the life standard of multifarious disease effected patients followed by healthy people. Following these advantages, digital healthcare demands a secure wireless communication infrastructure for interconnected self-empowered biosensor devices to maintain the trust of patients, doctors, pharmacologists, nursing staff, and other associated stakeholders, etc. Several authentications, privacy, and data preservation schemes had been used in the literature to ensure the security of this emerging technology, but with time, these counteraction prototypes become vulnerable to new security threats, as the hackers work tirelessly to compromise them and steal the legitimate information of user’s or disrupt the operation of an employed self-empowered wireless sensor network (SWSN). To discuss the security problems of SWSN applications, in this review article, we have presented a detailed survey of the present literature from 2019 to 2022, to familiarize the readers with different security threats and their counteraction schemes. Following this, we will highlight the pros and cons of these countermeasure techniques in the context of SWSN security requirements to underscore their limitations. Thereafter, we will follow-up the underlined limitations to discuss the open security challenges of SWSN that need the concerned authorities’ attention. Based on this, we will pave a road map for the future research work that could be useful for every individual associated with this technology. For the novelty and uniqueness of this work, we will make comparative analysis with present survey papers published on this topic to answer the question of reviewers, readers, editors, and students that why this paper is in time and needed in the presence of rival papers.

Satellite radio detection via dual-microwave Rydberg spectroscopy

Rydberg electric field sensors exploit the large number of Rydberg resonances to provide sensitivity over a broad range of the electromagnetic spectrum. However, due to the difficulty of accessing resonant Rydberg states at ultra-high frequency (UHF) and below, ubiquitous bands in the world's current wireless communications infrastructure, they currently fall, short in sensitivity in this range. We present a resonant Rydberg electric field sensor operating in the UHF band using a dual-optical dual-microwave spectroscopy scheme. Adding an additional microwave photon allows us to access transitions between Rydberg states with higher angular momentum (L=3→4), which have lower resonant frequencies than transitions typically used in Rydberg sensors. We discuss the applicability of this type of sensor across the UHF band and below and measure the resonant sensitivity of our system at 2.3 GHz to be 70(5) μV m−1 Hz−1∕2, 57 times better than the measured sensitivity with a far off-resonant probing scheme at this frequency. We also show the effectiveness of this sensing scheme by measuring Sirius XM satellite radio (2.320–2.345 GHz) received outside the laboratory and rebroadcast onto the atoms.

60 Gbps real-time wireless communications at 300 GHz carrier using a Kerr microcomb-based source

Future wireless communication infrastructure will rely on terahertz systems that can support an increasing demand for large-bandwidth, ultra-fast wireless data transfer. In order to satisfy this demand, compact, low-power, and low noise sources of terahertz radiation are being developed. A promising route to achieving this goal is combining photonic-integrated optical frequency combs with fast photodiodes for difference frequency generation in the THz. Here, we demonstrate wireless communications using a 300 GHz carrier wave generated via photomixing of two optical tones originating from diode lasers that are injection locked to a dissipative Kerr soliton frequency microcomb. We achieve transfer rates of 80 Gbps using homodyne detection and 60 Gbps transmitting simultaneously both data and clock signals in a dual-path wireless link. This experimental demonstration paves a path toward low-noise and integrated photonic millimeter-wave transceivers for future wireless communication systems.

Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning

The recent progress in blockchain and wireless communication infrastructures has paved the way for creating blockchain-based systems that protect data integrity and enable secure information sharing. Despite these advancements, concerns regarding security and privacy continue to impede the widespread adoption of blockchain technology, especially when sharing sensitive data. Specific security attacks against blockchains, such as data poisoning attacks, privacy leaks, and a single point of failure, must be addressed to develop efficient blockchain-supported IT infrastructures. This study proposes the use of deep learning methods, including Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM), to detect phishing attacks in a blockchain transaction network. These methods were evaluated on a dataset comprising malicious and benign addresses from the Ethereum blockchain dark list and whitelist dataset, and the results showed an accuracy of 99.72%.

Unmanned aerial vehicle: a review and future directions

The use of unmanned aerial vehicles (UAVs) will be crucial in the next generation of wireless communications infrastructure. When compared to traditional ground-based solutions, it is expected that their use in a variety of communication-based applications will increase coverage and spectrum efficiency. In this paper, we provide a detailed review of all relevant research works as follows. This paper presents types of UAVs (e.g., wireless coverage, military, agriculture, medical applications, environment, and climate, and delivery and transportation), characteristics of UAVs (e.g., node density, altering system topology, node mobility, radio broadcasting mode, frequency band, localization, and power consumption and network lifetime), the application of UAVs (e.g., Multi-UAV cooperation, UAV-to-VANET collaborations, and UAV-to-ground tasks). Additionally, this paper reviews the routing protocols of UAVs (e.g., topology-based, position-based, heterogeneous, delay-tolerant networks (DTNs), swarm-Based, and cluster-based) and simulation tools (e.g., OMNeT++, AVENS, MATLAB, NS3, SUMO, and OPNET). The design and development of any new methods for UAVs may use this work as a guide and reference.

A Mobile Self-Management App (CanSelfMan) for Children With Cancer and Their Caregivers: Usability and Compatibility Study.

Despite the increasing development of different smartphone apps in the health care domain, most of these apps lack proper evaluation. In fact, with the rapid development of smartphones and wireless communication infrastructure, many health care systems around the world are using these apps to provide health services for people without sufficient scientific efforts to design, develop, and evaluate them. The objective of this study was to evaluate the usability of CanSelfMan, a self-management app that provides access to reliable information to improve communication between health care providers and children with cancer and their parents/caregivers, facilitating remote monitoring and promoting medication adherence. We performed debugging and compatibility tests in a simulated environment to identify possible errors. Then, at the end of the 3-week period of using the app, children with cancer and their parents/caregivers filled out the User Experience Questionnaire (UEQ) to evaluate the usability of the CanSelfMan app and their level of user satisfaction. During the 3 weeks of CanSelfMan use, 270 cases of symptom evaluation and 194 questions were recorded in the system by children and their parents/caregivers and answered by oncologists. After the end of the 3 weeks, 44 users completed the standard UEQ user experience questionnaire. According to the children's evaluations, attractiveness (mean 1.956, SD 0.547) and efficiency (mean 1.934, SD 0.499) achieved the best mean results compared with novelty (mean 1.711, SD 0.481). Parents/caregivers rated efficiency at a mean of 1.880 (SD 0.316) and attractiveness at a mean of 1.853 (SD 0.331). The lowest mean score was reported for novelty (mean 1.670, SD 0.225). In this study, we describe the evaluation process of a self-management system to support children with cancer and their families. Based on the feedback and scores obtained from the usability evaluation, it seems that the children and their parents find CanSelfMan to be an interesting and practical idea to provide reliable and updated information on cancer and help them manage the complications of this disease.

Kernel-based online prediction algorithms for indoor localization in Internet of Things

Precise indoor position estimations of the targets play an increasingly important role in the enhancement of IoT-oriented applications. As an effective indoor localization technology, the fingerprint method has been extensively studied due to the widespread establishment of the wireless communication infrastructure. However, the fingerprint mismatch, which always leads to degradation of the positioning accuracy, commonly arises due to the diversity of the indoor environment and the existence of the abrupt noise in real-world applications. In this paper, a kernel adaptive filtering (KAF) assisted indoor position sensing framework based on the reproducing kernel Hilbert space (RKHS) model is exhibited, which is different from the conventional fingerprint-based positioning methods in two senses: first, in order to model a real-world environment, the linearity assumption in the traditional positioning approaches is replaced by the nonlinear counterpart; and second, a reproducing kernel function is employed to combat the impulsive noise and thus guaranteeing the robust positioning accuracy. Meanwhile, a generalized path-loss model is proposed to further enhance the accuracy performance of the KAF-based algorithms. The accuracy performance comparison between the proposed algorithms and widely-used machine learning methods such as KNN, naive Bayesian and the concept of transfer learning are carried out for different access points and different measurement areas. Experimental results exhibit that the proposed KAF framework-based algorithms can increase the positioning accuracy at least by 7% compared with the conventional fingerprint algorithms in representative indoor scenarios.

Role of Deep Learning in Mobile Ad-hoc Networks

The portable capability of MANETs has specially delighted in an unexpected expansion. A massive need for dynamic ad-hoc basis networking continues to be created by advancements in hardware design, high-speed growth in the wireless network communications infrastructure, and increased user requirements for node mobility and regional delivery processes. There are several challenging issues in mobile ad-hoc networks, such as machine learning method cannot analyze features like node mobility, channel variation, channel interference because of the absence of deep neural layers. Due to decentralized nature of mobile ad hoc networks, its necessitate to concentrate over some extremely serious issues like stability, scalability, routing based problems such as network congestion, optimal path selection, etc. and security.

An Anomaly Intrusion Detection for High-Density Internet of Things Wireless Communication Network Based Deep Learning Algorithms.

Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%.

Intelligent Dynamic Indoor Aerosol Sensing Using Terahertz Band Wireless Communication Systems

We investigate how an IRS (Intelligent Reflective Surface) deployment within a terahertz (THz) band communication system, can detect and classify indoor vapour clouds expelled in violent expiratory events, such as coughing. This detection is of interest for public health systems. Since indoor spaces are sites for deployment of wireless communication infrastructure in any case, combined sensing and communication is an attractive solution. Using 6G networks to accomplish this while not affecting communication performance, is a relevant and useful application of THz signal’s properties. We use machine learning to distinguish between violent expiratory events and other aerosol emissions.