Wireless Networks Research Articles

Deep Deterministic Policy Gradient-Based Resource Allocation Considering Network Slicing and Device-to-Device Communication in Mobile Networks.

Next-generation mobile networks, such as those beyond the 5th generation (B5G) and 6th generation (6G), have diverse network resource demands. Network slicing (NS) and device-to-device (D2D) communication have emerged as promising solutions for network operators. NS is a candidate technology for this scenario, where a single network infrastructure is divided into multiple (virtual) slices to meet different service requirements. Combining D2D and NS can improve spectrum utilization, providing better performance and scalability. This paper addresses the challenging problem of dynamic resource allocation with wireless network slices and D2D communications using deep reinforcement learning (DRL) techniques. More specifically, we propose an approach named DDPG-KRP based on deep deterministic policy gradient (DDPG) with K-nearest neighbors (KNNs) and reward penalization (RP) for undesirable action elimination to determine the resource allocation policy maximizing long-term rewards. The simulation results show that the DDPG-KRP is an efficient solution for resource allocation in wireless networks with slicing, outperforming other considered DRL algorithms.

Model-based reinforcement learning approach for federated learning resource allocation and parameter optimization

In this paper, we investigate the performance of a model-based approach for solving resource allocation and parameter adjustment problems in federated learning (FL) within a wireless network. Given the existence of models for energy, communication channels, and accuracy, such models can be leveraged to achieve improved performance. Additionally, machine learning techniques can be employed to identify known parts of the model and also exploit training data for unknown parts of the model, enabling the creation of complex policies. Model-based reinforcement learning (RL) methods have the potential to offer such solutions, particularly in resource allocation and parameter optimization settings where the model can be partially derived mathematically. Our results demonstrate that the use of such a method in FL scenarios leads to improvements in both performance and the number of iterations required to identify the desired policy. Our simulations demonstrate the significance of allocating appropriate resources for FL applications through proper consideration of inherent tradeoffs, as performance will not improve beyond a certain saturation point. Additionally, our proposed FL model takes intelligently into account the presence of slow users to propose efficient policies for users that may have access to more abundant resources.

Floor-Level Occupancy Estimation of a Multi-Story Building Using Coarse Wi-Fi Data

In recent years, there has been an extraordinary increase in wireless capable devices and network infrastructure, which spawned a corresponding rise in data produced from the interactions of these technologies. Mobile devices constantly roam, leading to a perpetual dialog between a mobile device and wireless access points. This dialogue generates a continuous stream of device-specific data, including but not limited to a device’s media access control address, time of access, and received signal strength. Given the knowledge of the access point’s location and received signal strength, it is possible to infer the position of user devices and estimate their mobility and occupancy. This article presents two methods for accurately measuring floor-level occupancy in a multi-story building at Texas State University using coarse Wi-Fi log data. The first method employs a static filter, while the second incorporates user-role data and user location to create a dynamic filter. Quantitative methods are used to evaluate these filters against field-collected reference data and existing internal people-counting sensors. Our results demonstrate that the dynamic filter, leveraging variable thresholds, provides a more accurate estimation of occupancy compared to the fixed 5-min static filter which consistently overestimated occupancy. This research sheds light on the potential of dynamic filters derived from user-role data for precise floor-level occupancy estimations, with implications for various applications.

A systematic review of design and performance enhancement techniques for the reduction of radar cross section in multiple input multiple output antennas

multiple-input multiple-output technology has emerged as a pivotal solution for addressing the capacity constraints of high-speed broadband wireless networks. By employing multiple antennas at both the transmitter and receiver, multiple-input multiple-output significantly enhances wireless communication efficiency. This paper presents a comprehensive review of multiple-input multiple-output antenna design strategies tailored for fifth-generation (5 G) networks and beyond. Specifically, it critically examines methodologies aimed at mitigating radar cross section in multiple-input multiple-output antennas, a crucial aspect of antenna engineering, especially in scenarios requiring reduced detectability. Various design and performance enhancement techniques proposed in the literature for RCS reduction in multiple-input multiple-output antennas are systematically analyzed, focusing on aspects such as antenna geometry and materials methods. Synthesizing these findings provides valuable insights into the current state of research, highlighting advancements, challenges, and future directions in developing efficient multiple-input multiple-output antennas with minimized radar cross section. This review contributes to antenna technology by aiding researchers and practitioners in informed decision-making regarding radar cross section reduction techniques for multiple-input multiple-output systems, and it identifies innovative approaches pertinent to multiple-input multiple-output applications. Finally, key considerations in multiple-input multiple-output antenna design are addressed, proposing potential directions for future research and development initiatives.

NDN multicast over wireless networks: A survey on fundamentals, challenges, and open issues

Wireless devices have shown remarkable growth in data demand in the last decade due to the proliferation of smart devices and the emergence of bandwidth-hungry applications. This increasing data traffic demand is overcrowding the radio frequency spectrum, leading wireless multicast schemes to become a popular research topic again, as they provide efficient data dissemination. NDN supports multicast communication by design. It is a networked system formed by named entities that adopt a communication model focusing on the content rather than its location. The architecture follows a receiver-driven communication model through which content consumers retrieve data through semantically meaningful names instead of specific destinations. Its properties are essential for multicast communication on ad hoc networks, as its features provide enhanced support for dynamic topologies, decentralized control, and the self-organization of participant nodes that communicate without needing a pre-existing network infrastructure. However, despite the wireless medium being broadcast by nature, NDN multicast is still challenging in wireless scenarios, especially in ad hoc environments, due to the node’s high mobility, link instability, constant handovers, and data transmission over a shared medium. Hence, this survey discusses the benefits of NDN for mobile scenarios through an in-depth analysis of NDN multicast features, focusing on fundamentals, challenges, and open issues when applied to wireless networking.

Formal estimation of wireless network services for signal strength and electromagnetic wave association in an International Green University

The United Nations focuses on 17 urgent problems to call for action in all countries. Goal 7 of the 17 urgent problems is based on affordable and clean energy. Since 2017, National Taitung University (NTTU) has dedicated more time and effort to attain the wisdom, health, sustainability and aesthetics as an international green university. To accomplish this, we adhere and construct a safe radiofrequency and electromagnetic wave environment to achieve healthy and sustainable campus objectives. According to the UI GreenMetric World University Rankings, NTTU was ranked 74th in 2021, 67th in 2022 and 58th in 2023. In this study, we propose a formal estimation of wireless network services for classrooms or smart spaces to achieve the goal of safe radiofrequency and electromagnetic waves. Inside classrooms or smart spaces, better wireless signal strength and safer electromagnetic waves are achieved. Moreover, the proposed method can be used to determine the quantity of wireless access points for a given classroom or smart space to avoid unsafe electromagnetic waves and inappropriate energy consumption. The experimental results show that all benchmarks meet the wireless exposure limits of the WHO and physician safe technologies in the NTTU.

Real-time optical-wireless cooperative switching on wireless quality degradation for end-to-end video delay guarantee

This paper proposes an optical-wireless cooperative control method to guarantee end-to-end delays in the case of radio quality degradation in wireless networks by switching optical paths. The method leverages the control information from mobile systems to dynamically switch the optical path between the distributed unit (DU) and the far aggregated central unit (CU) to another path forward to the nearest virtualized central unit (vCU) at the edge sight, on the basis of the radio quality. This enables demanding applications that require a combination of high bandwidth and low latency, such as remote operation using drones. Preliminary simulations were conducted to investigate the effect of quality degradation in the wireless domain and the potential effectiveness of the proposed delay compensation method. In the experiments conducted to verify the feasibility of the proposed method, the results demonstrated that the optical path switching was successfully performed without packet loss and within 4 ms from the transmission of the control frame. The proposed method was also able to correctly switch back when the processing rate of mobile edge computing (MEC) was exceeded after the radio quality recovered. This study demonstrates the effectiveness of further coordination between wired and wireless control for a wider range of real-time applications using video.

An efficient three-dimensional node localization using recurrent neural networks in unmanned aerial vehicle-assisted wireless networks: an optimization perspective

PurposeUnlike many existing methods that are primarily focused on two-dimensional localization, this research paper extended the scope to three-dimensional localization. This enhancement is particularly significant for unmanned aerial vehicle (UAV) applications that demand precise altitude information, such as infrastructure inspection and aerial surveillance, thereby broadening the applicability of UAV-assisted wireless networks.Design/methodology/approachThe paper introduced a novel method that employs recurrent neural networks (RNNs) for node localization in three-dimensional space within UAV-assisted wireless networks. It presented an optimization perspective to the node localization problem, aiming to balance localization accuracy with computational efficiency. By formulating the localization task as an optimization challenge, the study proposed strategies to minimize errors while ensuring manageable computational overhead, which are crucial for real-time deployment in dynamic UAV environments.FindingsSimulation results demonstrated significant improvements, including a channel capacity of 99.95%, energy savings of 89.42%, reduced latency by 99.88% and notable data rates for UAV-based communication with an average localization error of 0.8462. Hence, the proposed model can be used to enhance the capacity of UAVs to work effectively in diverse environmental conditions, offering a reliable solution for maintaining connectivity during critical scenarios such as terrestrial environmental crises when traditional infrastructure is unavailable.Originality/valueConventional localization methods in wireless sensor networks (WSNs), such as received signal strength (RSS), often entail manual configuration and are beset by limitations in terms of capacity, scalability and efficiency. It is not considered for 3-D localization. In this paper, machine learning such as multi-layer perceptrons (MLP) and RNN are employed to facilitate the capture of intricate spatial relationships and patterns (3-D), resulting in enhanced localization precision and also improved in channel capacity, energy savings and reduced latency of UAVs for wireless communication.

A review on free space optical communication links: 5G applications

Abstract Free space optical (FSO) communications has garnered significant attention as a potential substitute for radio frequency (RF) wireless communication in short and long range transmissions. Free-space optical (FSO) communication is a method where an optical beam is sent across an unguided channel, gaining favour in high-speed wireless networks due to its large optical bandwidth, fast data transmission, lack of licencing requirements, secure nature, and easy setup. Despite several inherent advantages that FSO has over existing radio frequency (RF) communication technologies, its link performance is severely hampered by atmospheric fog and turbulence, which not only reduce optical irradiance but also cause the modulated optical beam to deviate from its intended path, impacting the quality of the received signal. This review paper discusses FSO technology, channel impairments under varied atmospheric attenuation conditions, advantages, and applications. Various channel models in terms of probability density functions are described, which characterise the statistical nature of the irradiance fluctuations of optical transmission through scattering and atmospheric turbulence. Key objectives that need to be addressed in the near future, particularly in fifth-generation (5G) and beyond, includes enhancing capacity, boosting data rates, minimising latency, and enhancing service quality. Last but not the least, several uses of FSO that facilitates 5G and future technologies have also been covered in this study.

Network traffic prediction based on PSO-LightGBM-TM

Network traffic prediction is critical in wireless network management by allowing a good estimate of the traffic trend, which is also an important approach for detecting traffic anomalies in order to enhance network security. Deep-learning-based method has been widely adopted to predict network traffic matrix (TM) though with the main drawbacks in high complexity and low efficiency. In this paper, we propose a traffic prediction model based on Particle Swarm Optimization (PSO) and LightGBM (PSO-LightGBM-TM), which optimizes the LightGBM parameters for each network flow by PSO so that LightGBM can adapt to each of the network traffic flow. Compared with existing commonly used deep learning models, our model has a more straightforward structure and yet outperforms existing deep learning models. Sufficient comparison tests on three real network traffic datasets, Abilene, GÉANT, and CERNET have been conducted, and the results show that our model provides more accurate results and higher prediction efficiency.

Leveraging Propagation Delay for Wormhole Detection in Wireless Networks

Leveraging Propagation Delay for Wormhole Detection in Wireless Networks

An efficient federated learning solution for the artificial intelligence of things

Federated Learning (FL) has gained popularity due to its advantages over centralized learning. However, existing FL research has primarily focused on unconstrained wired networks, neglecting the challenges posed by wireless Internet of Things (IoT) environments. The successful integration of FL into IoT networks requires tailored adaptations to address unique constraints, especially in computation and communication. This paper introduces Communication-Aware Federated Averaging (CAFA), a novel algorithm designed to enhance FL operations in wireless IoT networks with shared communication channels. CAFA primarily leverages the latent computational capacities during the communication phase for local training and aggregation. Through extensive and realistic evaluations in dedicated FL-IoT framework, our method demonstrates significant advantages over state-of-the-art approaches. Indeed, CAFA achieves up to a 4x reduction in communication costs and accelerates FL training by as much as 70%, while preserving model accuracy. These achievements position CAFA as a promising solution for the efficient implementation of FL in constrained wireless networks.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

AoU-Based Local Update and User Scheduling for Semi-Asynchronous Online Federated Learning in Wireless Networks

Investigating the impact of body node coordinator position on communication reliability in wireless body area networks

Investigating the impact of body node coordinator position on communication reliability in wireless body area networks

A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks

A Review on Machine Learning Assisted Handover Mechanisms for Future Generation Wireless Networks

Scalable Creditable-Committee-Based Blockchain Consensus Protocol for Multihop Wireless Networks

Scalable Creditable-Committee-Based Blockchain Consensus Protocol for Multihop Wireless Networks

Electromagnetic Field Exposure-Aware AI Framework for Integrated Sensing and Communications-Enabled Ambient Backscatter Wireless Networks

Electromagnetic Field Exposure-Aware AI Framework for Integrated Sensing and Communications-Enabled Ambient Backscatter Wireless Networks

Comprehensive Assessment of Context-Adaptive Street Lighting: Technical Aspects, Economic Insights, and Measurements from Large-Scale, Long-Term Implementations.

This paper addresses the growing importance of efficient street lighting management, driven by rising electricity costs and the need for municipalities to implement cost-effective solutions. Central to this study is the UNI 11248 Italian regulation, which extends the European EN 13201-1 standard introduced in 2016. These standards provide guidelines for designing, installing, operating, and maintaining lighting systems in pedestrian and vehicular traffic areas. Specifically, the UNI 11248 standard introduces the possibility to dynamically adjust light intensity through two alternative operating modes: (a) Traffic Adaptive Installation (TAI), which dims the light based solely on real-time traffic flow measurements; and (b) Full Adaptive Installation (FAI), which, in addition to traffic measurements, also requires evaluating road surface luminance and meteorological conditions. In this paper, we first present the general architecture and operation of an FAI-enabled lighting infrastructure, which relies on environmental sensors and a heterogeneous wireless communication network to connect intelligent, remotely controlled streetlights. Subsequently, we examine large-scale, in-field FAI infrastructures deployed in Vietnam and Italy as case studies, providing substantial measurement data. The paper offers insights into the measured energy consumption of these infrastructures, comparing them to that of conventional light-control strategies used in traditional installations. The measurements demonstrate the superiority of FAI as the most efficient solution.

Exploring the potential of 5G uplink communication: Synergistic integration of joint power control, user grouping, and multi-learning Grey Wolf Optimizer

Non-orthogonal Multiple Access (NOMA) techniques offer potential enhancements in spectral efficiency for 5G and 6G wireless networks, facilitating broader network access. Central to realizing optimal system performance are factors like joint power control, user grouping, and decoding order. This study investigates power control and user grouping to optimize spectral efficiency in NOMA uplink systems, aiming to reduce computational difficulty. While previous research on this integrated optimization has identified several near-optimal solutions, they often come with considerable system and computational overheads. To address this, this study employed an improved Grey Wolf Optimizer (GWO), a nature-inspired metaheuristic optimization method. Although GWO is effective, it can sometimes converge prematurely and might lack diversity. To enhance its performance, this study introduces a new version of GWO, integrating Competitive Learning, Q-learning, and Greedy Selection. Competitive learning adopts agent competition, balancing exploration and exploitation and preserving diversity. Q-learning guides the search based on past experiences, enhancing adaptability and preventing redundant exploration of sub-optimal regions. Greedy selection ensures the retention of the best solutions after each iteration. The synergistic integration of these three components substantially enhances the performance of the standard GWO. This algorithm was used to manage power and user-grouping in NOMA systems, aiming to strengthen system performance while restricting computational demands. The effectiveness of the proposed algorithm was validated through numerical evaluations. Simulated outcomes revealed that when applied to the joint challenge in NOMA uplink systems, it surpasses the spectral efficiency of conventional orthogonal multiple access. Moreover, the proposed approach demonstrated superior performance compared to the standard GWO and other state-of-the-art algorithms, achieving reduced system complexity under identical constraints.