Wireless Channel Resources Research Articles

Cluster-based Data Sharing for Web 3.0 in Intelligent Transportation Systems

Intelligent transportation system (ITS) applications are dependent on secure and robust wireless data sharing among vehicles and roadside units (RSUs). Multiple types of data are shared among the ITS devices which include safety information, road services, web based information retrieval and task computation. Web 3.0 offers a decentralized, distributed and secure data sharing mechanism for ITSs. Allocation of wireless channel resources are critical to enable an efficient ITS system. In this paper, a novel data sharing technique for Web 3.0 based ITS is presented that relies on an intelligent clustering algorithm. In the first step, the proposed technique uses a K-means algorithm to find groups of vehicles with similar speeds. In the second step, each cluster is assigned an RSU which has the highest average data rate with all vehicles in the cluster. This is achieved by using a stable matching technique so that there is no contention and each cluster is assigned a separate RSU. The algorithm periodically updates the clusters and RSU allocation for web data sharing between vehicles and RSUs. Simulation results show that the proposed clustering-based data sharing technique improves sum-rate by 20% and reduces network delay by 23%.

A lightweight deep learning architecture for automatic modulation classification of wireless internet of things

AbstractThe wireless Internet of Things (IoT) is widely used for data transmission in power systems. Wireless communication is an important part of the IoT. The existing modulation classification algorithms have low classification accuracy when facing strong electromagnetic interference, which causes decoding error link interruption and wastes wireless channel resources. Therefore, it is necessary to study signal modulation classification methods in a low signal‐to‐noise ratio (SNR) environment. In this paper, a lightweight Deep Neural Networks (DNNs) modulation classification method based on the Informer architecture classifier and two‐dimensional (2‐D) curves input of the spectral correlation function (SCF) is proposed, which uses in‐phase and quadrature (I/Q) signals to generate 2‐D cross‐section SCF curve first and then feeds the feature curve into the Informer network to classify the modulation method. This model can better learn the robustness characteristics in a long sequence. Through testing, the classification accuracy of the modulation signal is not much lower than that of the current good classification method when the SNR is 10 dB, and this method can still show higher accuracy when hardware resources are limited. It is a compact design of a modulation classification model and easy to deploy on low‐cost embedded platforms.

Support of resourses redistribution in NB-IоT LTE networks

The NB-IoT is expected to be used with the deployed LTE network as well as future 5G networks. The rapid development of the IoT concept has entailed the need to provide wireless communication to a huge number of devices included in the infrastructure. As part of the 5G NR standard for such devices, Massive Machine Communications (m MTC) technology is focused on optimizing the use of network resources to support a large number of stable connections per unit area. The Narrowband Internet of Things (NB-IoT) has been analyzed to enable the connectivity of a wide range of new IoT devices and services to the mobile network. The NB-IoT is also shown to be designed for fixed devices with low data transmission and low consumption, leading to an increase in the number of interconnected devices. In turn, standard NB-IoT modules attempting to simultaneously request radio channel resources for uplink data transmission may suffer from random access preamble collision. The proposed model describes the macro- and microcells of an NB-IoT LTE cluster. An increase in the efficiency of using the bandwidth of networks based on macro- and microcells with a high concentration of users of the NB-IoT LTE networks is shown. Numerical results of an analysis of identifying factors affecting system performance are presented. A linear increase in throughput has been revealed depending on the throughput of the macrocell when using the shared resource of the macrocell for a microcell of equal size without prior repacking of channels when servicing moving subscribers. In the absence of moving subscribers, the additional load is serviced, and the efficiency of using a macro cell increases almost 3 times. In addition, repacking channels significantly increases system throughput.

The Analysis of the Usage Efficiency of the Time-frequency Resources for Different 5G NR CORESET Configuration Settings

The 5G mobile networks is a New Radio for finding wireless access solutions for Internet access of users with the most demanding requirements for quality service. To implement it radio interface resource allocation functions implemented by hardware and software vendors at base stations must notify subscribers about their decisions with so-called control channels the functions of which are distributed in the CORESET configuration area. The settings of the CORESET parameters affect the efficiency of using radio channel resources. This article discusses CORESET issues and their impact on the efficiency of using radio resources.

Medium access in cloud-based for the internet of things based on mobile vehicular infrastructure

Smart cities are made up of a large number of smart, intelligent gadgets that can sense, compute, act, and communicate. Focusing on how data is transferred between sensory devices and applications in the internet of things (IoT), and cyber-physical systems have led to 5G/IoT integration. This paper proposes a revolutionary architecture for mobile vehicular cloud infrastructure that takes variable weather, road, and traffic circumstances into consideration. It proposes a dynamic speed management system for smart cities. To optimize system flexibility and reduce costs, the system makes advantage of the most recent advancements in wireless communication and utilizes current telecommunication infrastructures utilized in data streaming, sound, and video. The study presents an internet protocol (IP) real-time subsystem-network-based framework for requesting bandwidth from free wireless channel resources using the channell quality indicator channel.

Multi-armed bandit based device scheduling for crowdsensing in power grids

With the increase of devices in power grids, a critical challenge emerges on how to collect information from massive devices, as well as how to manage these devices. Mobile crowdsensing is a large-scale sensing paradigm empowered by ubiquitous devices and can achieve more comprehensive observation of the area of interest. However, collecting sensing data from massive devices is not easy due to the scarcity of wireless channel resources and a large amount of sensing data, as well as the different capabilities among devices. To address these challenges, device scheduling is introduced which chooses a part of mobile devices in each time slot, to collect more valuable sensing data. However, the lack of prior knowledge makes the device scheduling task hard, especially when the number of devices is huge. Thus the device scheduling problem is reformulated as a multi-armed bandit (MAB) program, one should guarantee the participation fairness of sensing devices with different coverage regions. To deal with the multi-armed bandit program, a device scheduling algorithm is proposed on the basis of the upper confidence bound policy as well as virtual queue theory. Besides, we conduct the regret analysis and prove the performance regret of the proposed algorithm with a sub-linear growth under certain conditions. Finally, simulation results verify the effectiveness of our proposed algorithm, in terms of performance regret and convergence rate.

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

User Selection for Federated Learning in a Wireless Environment: A Process to Minimize the Negative Effect of Training Data Correlation and Improve Performance

This article describes our study about federated learning by using the front view-based training process in autonomous driving as an example. The overlap of camera views among users creates training data correlation. It is shown that this correlation undermines the optimization performance of the federated learning process. To reduce the negative effect of correlation within training data while limiting wireless channel resource utilization, a federated learning protocol with user selection is proposed. The protocol selects a limited number of users with sufficient spatial separation in each round of federated learning. An exclusion zone is applied to maintain separation during user selection. Experimental results show that in an example deployment scenario with a user density of 0.05, applying a discrete exclusion zone (DEZ) to prevent selecting the first three nearest users and applying a geographical exclusion zone (DEZ) to avoid selecting users within 70 m have equivalent effects on reducing training data. Furthermore, both methods can provide the performance of five-user federated learning approaches, an ideal case without correlation in the training data.

The prospects for radiofrequency electromagnetic fields control approaches improvement under 5G wireless communication technologies introduction

5G mobile communication system networks improvement leads to scenarios for the use of electromagnetic energy in various sectors of the economy variety increase and radiofrequency electromagnetic fields (EMF) person’ exposure with expansion of the exposed by occupational and non-occupational exposure contingent including. Base station EMF distinctive feature is its complex frequency-time and spatial dynamics with constant control signals that should be taken into account in modern EMF control methods. International practice in case of cellular base stations EMF assessment uses maximal values determination: theoretical and actual levels. EMF theoretical maximal values characterize base station operation mode under highest network load, highest data traffic and time-frequency radio channel resource full usage at the maximum permitted transmission power. Actual maximum EMF levels determination approach is base stations EMF evaluation alternative principle and is based on practically achievable maximum EMF emission, stochastic nature of base station signals with taking into account. The approaches to statistical assessment of actual maximum exposure levels are becoming a priority for international practice of base station EMF assessment and control due to adaptive antenna technologies in 5G cellular networks development. This approach to realistic EMF exposure assessment is fundamentally new for Russian practice and will require methodological updating of regulatory framework as well as comprehensive researches with network operators, including approaches to selective measurement results extrapolation for implementation. Ethics. The study did not require the ethics committee conclusion.

Using self-deferral to achieve fairness between Wi-Fi and NR-U in downlink and uplink scenarios

Wireless networks operating in unlicensed bands generally use one of two channel access paradigms: random access (e.g., Wi-Fi) or scheduled access (e.g., LTE License Assisted Access, LTE LAA and New Radio-Unlicensed, NR-U). The coexistence between these two paradigms is based on listen before talk (LBT), which was, however, designed for random access. Meanwhile, scheduled systems require that their transmissions start at the beginning of a slot boundary. Synchronizing this boundary to the end of LBT usually requires transmitting a reservation signal (RS) to block the channel. Since the RS is a waste of channel resources, we investigate an alternative self-deferral approach (gap-based access) using analytical and simulation models. We put forth a proposal to employ only self-deferral, treat the gap mechanism as a partial backoff, and adjust the contention window (CW) settings to the number of coexisting nodes. We demonstrate that this approach not only ensures fairness in Wi-Fi/NR-U coexistence but also avoids wasting radio channel resources and improves aggregate network throughput. Furthermore, we show that the proposed approach outperforms RS-based access and provides significant throughput and fairness gains. Finally, we implement a long short-term memory-based (LSTM) regression model to predict those Wi-Fi/NR-U CW settings which lead to coexistence fairness.

MAC Protocol Analysis for Wireless Sensor Networks

In wireless sensor networks, MAC protocol is used to achieve efficient, fair and balanced allocation of wireless channel resources and access control of nodes in the network, and to control the communication process of nodes in the network. The energy consumption of wireless sensor nodes is mainly concentrated in the communication unit, including: data transceiver, idle listening, protocol control overhead, etc. A good MAC protocol is beneficial to reduce unnecessary energy consumption and enhance node life time. At the same time, MAC protocol, as the bottom protocol of wireless sensor network, provides a stable and reliable communication foundation for the realization of the upper protocol. Therefore, a large number of scholars at home and abroad have carried out extensive and in-depth research on MAC layer protocol of network. In this paper, the research process and progress of MAC layer protocol in wireless sensor networks are analyzed, and the research progress of MAC layer protocol in multi-radio frequency and multi-channel wireless sensor networks is analyzed.

A distributed e-health management model with edge computing in healthcare framework

Edge healthcare system is recognized as an acceptable paradigm for resolving this problem. The IoMT is divided into two sub-networks - intraWBANs and beyond-WBANs - based on the physical bonds of WBANs. Given the features of the healthcare systems, medical emergency, AoI and power depreciation are the prices of MUs. Intra-WBANs, a cooperative game shapes the wireless channel resource allocation problem. The Nash negotiation solution is used to get the unique optimum point in Pareto. MUs are regarded reasonable and perhaps egoistic in non-WBANs. Another non-cooperative activity is therefore developed to reduce overall system costs. The assessments of the performance of the system-wide cost and of the number of MUs gaining from edge computer systems are done to illustrate the success of our solution. Finally, for further effort, numerous barriers to research and open questions are highlighted.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

Mobile Edge Computing Enabled 5G Health Monitoring for Internet of Medical Things: A Decentralized Game Theoretic Approach

The prompt evolution of Internet of Medical Things (IoMT) promotes pervasive in-home health monitoring networks. However, excessive requirements of patients result in insufficient spectrum resources and communication overload. Mobile Edge Computing (MEC) enabled 5G health monitoring is conceived as a favorable paradigm to tackle such an obstacle. In this paper, we construct a cost-efficient in-home health monitoring system for IoMT by dividing it into two sub-networks, i.e., intra-Wireless Body Area Networks (WBANs) and beyond-WBANs. Highlighting the characteristics of IoMT, the cost of patients depends on medical criticality, Age of Information (AoI) and energy consumption. For intra-WBANs, a cooperative game is formulated to allocate the wireless channel resources. While for beyond-WBANs, considering the individual rationality and potential selfishness, a decentralized non-cooperative game is proposed to minimize the system-wide cost in IoMT. We prove that the proposed algorithm can reach a Nash equilibrium. In addition, the upper bound of the algorithm time complexity and the number of patients benefiting from MEC is theoretically derived. Performance evaluations demonstrate the effectiveness of our proposed algorithm with respect to the system-wide cost and the number of patients benefiting from MEC.

Система вивантаження даних за технологією D2D у неліцензованому діапазоні частот у складі систем зв’язку 5G

With a constant increase in the number of users and the amount of data that they transmit among themselves, modern communication system networks cannot cope with the download. As an alternative to network compression, to increase the capacity of the network, mobile traffic is unloaded in the form of a direct connection D2D (device-to-device) between the equipment of users who are in close proximity to each other. Thus, D2D interaction is one of the most promising areas of development of communication systems, because it is becoming a new component in addition to the mobile network. It is important that this technology ensures the interaction of mobile network users without the use of a centralized infrastructure, which reduces its load. This interaction allows you to upload data streams from the mobile network to the D2D system, which can operate at unlicensed frequencies. As a result, the D2D system provides shorter connections for communications, increased energy efficiency and better traffic flow management. The development of D2D technology is a simple and reliable way to increase the bandwidth of mobile networks without the need to change its structure. But at the same time, several features arise that are related to the consideration of the interference and the distribution of radio channel resources. In this work, we consider a model of a D2D direct connection system with mobile support that serves streams of user data (sessions), as well as a way to control the downloading of traffic from mobile network to a D2D connection. The above theoretical and simulated results allow us to study communication system with the possibility of D2D interaction, to evaluate their capacity and energy efficiency. The presented mathematical approach takes into account the dynamics of user traffic in time and the placement of system nodes in space, which is confirmed by simulation results.

Edge Computing Based Healthcare Systems: Enabling Decentralized Health Monitoring in Internet of Medical Things

The rapid development of IoMT facilitates pervasive healthcare networks. However, numerous MUs lead to deficient wireless channel and computation resources. Edge computing based healthcare system is appreciated as a satisfactory paradigm to solve this obstacle. Based on the physical bound of WBANs, the IoMT is separated into two sub-networks, that is, intra- WBANs and beyond-WBANs. Considering the characteristics of healthcare systems, the cost of MUs relies on medical urgency, AoI and energy dissipation. In intra-WBANs, the wireless channel resource allocation problem is modeled by a cooperative game. The Nash bargaining solution is utilized to obtain the unique Pareto optimal point. In beyond-WBANs, MUs are considered to be rational and potentially selfish. Thus, a non-cooperative game is formulated to minimize the system-wide cost. To demonstrate the effectiveness of our proposed solution, performance evaluations are conducted with respect to the system-wide cost and the number of MUs benefiting from edge computing. Finally, several research challenges and open issues are discussed for further work.

Perceptual QoE-Optimal Resource Allocation for Adaptive Video Streaming

Video streaming in mobile environments has always been challenging due to various factors. The time-varying wireless channel, limited and shared transmission resources, fluctuating network conditions between the video server and the end user etc. greatly affect the timely delivery of videos. Given these factors, it is important that the wireless networks perform optimal allocation of resources and cater to the demands of the video streaming users without degrading their quality-of-experience (QoE). Modeling streaming QoE as perceived subjectively by the users is non-trivial, and in general a complex task, as it is continuous, dynamic, and time-varying in nature. The continuous perceptual QoE degradation due to network induced artifacts such as time-varying video quality and rebuffering events has not been considered in the literature for resource allocation (RA). In this paper, we propose Video Quality Aware Resource Allocation (ViQARA), a perceptual QoE based RA algorithm for video streaming in cellular networks. ViQARA leverages the strength of the latest continuous QoE models and integrates it with the generalized $\alpha $ -fair strategy for RA. Through extensive simulations, we demonstrate that ViQARA can provide significant improvement in the users perceptual QoE as well as a remarkable reduction in the number of rebufferings when compared to existing throughput based RA methods. The proposed algorithm is also shown to provide better QoE optimization of the available resources in general, and especially so when the cellular network is resource constrained and/or experiences large packet delays.

Efficient and secure attribute-based heterogeneous online/offline signcryption for body sensor networks based on blockchain

In body sensor networks, both wearable and implantable biosensors are deployed in a patient body to monitor and collect patient health record information. The health record information is then transmitted toward the medical server via a base station for analysis, diagnosis, and treatment by medical experts. Advancement in wireless technology although improves the patient health–monitoring mechanism, but still there are some limitations regarding security, privacy, and efficiency due to open wireless channel and limited resources of body sensor networks. To overcome these limitations, we have proposed an efficient and secure heterogeneous scheme for body sensor networks, in which biosensor nodes use a certificate-less cryptography environment to resolve the key escrow and certificate-management problems, while MS uses a public key infrastructure environment to enhance the scalability of the networks. Furthermore, we design an online/offline signcryption method to overcome the burden on biosensor nodes. We split the signcryption process into two phases: offline phase and online phase. In the offline phase, the major operations are computed without prior knowledge of patient data. While in online phase, the minor operations are computed when patient data are known. Besides, we have used a new hybrid blockchain technology approach for the secure transmission of patient information along with attributes stored in the medical server toward the cloud that provides ease of patient data access remotely from anywhere by the authorized users and data backup in case of medical server failure. Moreover, hybrid blockchain provides advantages of interoperability, transparency traceability, and universal access. The formal security analysis of the proposed scheme is proved in the standard model, and informal security assures that our scheme provides resistance against possible attacks. As compared to other existing schemes, our proposed scheme consumes fewer resources and efficient in terms of processing cost, transmission overhead, and energy consumption.

A QoE-Oriented Uplink Allocation for Multi-UAV Video Streaming

Video streaming has become a kind of main information carried by Unmanned Aerial Vehicles (UAVs). Unlike single transmission, when a cluster of UAVs execute the real-time video shooting and uploading mission, the insufficiency of wireless channel resources will lead to bandwidth competition among them and the competition will bring bad watching experience to the audience. Therefore, how to allocate uplink bandwidth reasonably in the cluster has become a crucial problem. In this paper, an intelligent and distributed allocation mechanism is designed for improving users’ video viewing satisfication. Each UAV in a cluster can independently adjust and select its video encoding rate so as to achieve flexible uplink allocation. This choice relies neither on the existence of the central node, nor on the large amount of information interaction between UAVs. Firstly, in order to distinguish video service from ordinary data, a utility function for the overall Quality of Experience (QoE) is proposed. Then, a potential game model is built around the problem. By a distributed self-learning algorithm with low complexity, all UAVs can iteratively update their own bandwidth strategy in a short time until equilibria, thus achieving the total quality optimization of all videos. Numeric simulation results indicate, after a few iterations, that the algorithm converges to a set of correlation equilibria. This mechanism not only solves the uplink allocation problem of video streaming in UAV cluster, but also guarantees the wireless resource providers in distinguishing and ensuring network service quality.

Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.