Hasil untuk "Cellular telephone services industry. Wireless telephone industry"

Hailiang Zhao, Ziqi Wang, Daojiang Hu et al.

The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.

Ali Sufyan, Khan Bahadar Khan, O. A. Khashan et al.

The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

Aida Aminian, Shahin Kamali, Seyed-Mohammad Seyed-Javadi et al.

In the Telephone Broadcasting problem, the goal is to disseminate a message from a given source vertex of an input graph to all other vertices in the minimum number of rounds, where at each round, an informed vertex can send the message to at most one of its uninformed neighbors. For general graphs of n vertices, the problem is NP-complete, and the best existing algorithm has an approximation factor of O(log n/ log log n). The existence of a constant factor approximation for the general graphs is still unknown. In this paper, we study the problem in two simple families of sparse graphs, namely, cacti and graphs of bounded pathwidth. There have been several efforts to understand the complexity of the problem in cactus graphs, mostly establishing the presence of polynomial-time solutions for restricted families of cactus graphs. Despite these efforts, the complexity of the problem in arbitrary cactus graphs remained open. We settle this question by establishing the NP-completeness of telephone broadcasting in cactus graphs. For that, we show the problem is NP-complete in a simple subfamily of cactus graphs, which we call snowflake graphs. These graphs not only are cacti but also have pathwidth 2. These results establish that, despite being polynomial-time solvable in trees, the problem becomes NP-complete in very simple extensions of trees. On the positive side, we present constant-factor approximation algorithms for the studied families of graphs, namely, an algorithm with an approximation factor of 2 for cactus graphs and an approximation factor of O(1) for graphs of bounded pathwidth.

Ankita Singh, Shiv Prakash, Sudhakar Singh

Wireless networking is encouraged by the constant enhancement of sensors' ability and wireless communication. To provide service quality support for multimedia viz. audio and video streams, the IEEE 802.11e MAC (Media Access Control) improves basic 802.11 MAC. IEEE 802.11 standard series such as IEEE 802.11a, b, g, n, p, and ac have been promoted and specified in the current communications and connection development. Each standard has functionality that matches the kind of applications for which the standard is intended. IEEE 802.11ac has better performance with fewer interferences and achieves gigabits per second capacity transfer rates. This paper discusses the comparative examination of the IEEE 802.11a, IEEE 802.11b, IEEE 802.11g, IEEE 802.11n, IEEE 802.11p, and IEEE 802.11ac standards which increase accuracy and performance pertaining to the IEEE 802.11 standard. In this paper, we investigate the design requirements for numerous simultaneous peer-to-peer connections. Further, this study offers a systematic review and analysis of the MAC layer in WMN (Wireless Mesh Network) and also highlights their open research issues and challenges. Finally, this paper discusses various potential directions for future research in this area with an emphasis on their strengths and limitations.

Daniel Hathcock, Guy Kortsarz, R. Ravi

We consider minimum time multicasting problems in directed and undirected graphs: given a root node and a subset of $t$ terminal nodes, multicasting seeks to find the minimum number of rounds within which all terminals can be informed with a message originating at the root. In each round, the telephone model we study allows the information to move via a matching from the informed nodes to the uninformed nodes. Since minimum time multicasting in digraphs is poorly understood compared to the undirected variant, we study an intermediate problem in undirected graphs that specifies a target $k < t$, and requires the only $k$ of the terminals be informed in the minimum number of rounds. For this problem, we improve implications of prior results and obtain an $\tilde{O}(t^{1/3})$ multiplicative approximation. For the directed version, we obtain an {\em additive} $\tilde{O}(k^{1/2})$ approximation algorithm (with a poly-logarithmic multiplicative factor). Our algorithms are based on reductions to the related problems of finding $k$-trees of minimum poise (sum of maximum degree and diameter) and applying a combination of greedy network decomposition techniques and set covering under partition matroid constraints.

Filipo Sharevski, Jennifer Vander Loop, Bill Evans et al.

This paper reports on a study exploring how two groups of individuals, legally blind (n=36) and sighted ones (n=36), react to aural telephone scam warnings in naturalistic settings. As spoofing a CallerID is trivial, communicating the context of an incoming call instead offers a better possibility to warn a receiver about a potential scam. Usually, such warnings are visual in nature and fail to cater to users with visual disabilities. To address this exclusion, we developed an aural variant of telephone scam warnings and tested them in three conditions: baseline (no warning), short warning, and contextual warning that preceded the scam's content. We tested the two most common scam scenarios: fraud (interest rate reduction) and identity theft (social security number) by cold-calling participants and recording their action, and debriefing and obtaining consent afterward. Only two participants "pressed one" as the scam demanded, both from the legally blind group that heard the contextual warning for the social security scenario. Upon close inspection, we learned that one of them did so because of accessibility issues with their screen reader and the other did so intentionally because the warning convinced them to waste the scammer's time, so they don't scam vulnerable people. Both the legally blind and the sighted participants found the contextual warnings as powerful usable security cues that, together with STIR/SHAKEN indicators like "Scam Likely", would provide robust protection against any type of scam. We also discussed the potential privacy implications of the contextual warnings and collected recommendations for usably accessible implementation.

Seyed Amin Tabatabaei, Sarah Fancher, Michael Parsons et al.

We address the task of hierarchical multi-label classification (HMC) of scientific documents at an industrial scale, where hundreds of thousands of documents must be classified across thousands of dynamic labels. The rapid growth of scientific publications necessitates scalable and efficient methods for classification, further complicated by the evolving nature of taxonomies--where new categories are introduced, existing ones are merged, and outdated ones are deprecated. Traditional machine learning approaches, which require costly retraining with each taxonomy update, become impractical due to the high overhead of labelled data collection and model adaptation. Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification. However, applying them to large and dynamic taxonomies presents unique challenges as the vast number of labels can exceed LLMs' input limits. In this paper, we present novel methods that combine the strengths of LLMs with dense retrieval techniques to overcome these challenges. Our approach avoids retraining by leveraging zero-shot HMC for real-time label assignment. We evaluate the effectiveness of our methods on SSRN, a large repository of preprints spanning multiple disciplines, and demonstrate significant improvements in both classification accuracy and cost-efficiency. By developing a tailored evaluation framework for dynamic taxonomies and publicly releasing our code, this research provides critical insights into applying LLMs for document classification, where the number of classes corresponds to the number of nodes in a large taxonomy, at an industrial scale.

International Journal of Antennas and Propagation

D. Allin Joe, Thiyagarajan Krishnan

This paper presents the implementation of a triple band microstrip patch antenna based on modified split ring resonator (SRR) and modified complementary split ring resonator (CSRR) as defected ground plane. The antenna is designed in a FR4 substrate of dielectric constant 4.4 with dimensions as 35 mm × 35 mm × 1.6 mm that comprises a total designed antenna volume of 1960 mm3. Multiband operation is introduced in the antenna through multiple resonators that are introduced in the patch. Impedance matching was provided with the help of partial ground plane and modified SRR in patch. The simulation performance of the antenna is verified using high frequency structural simulator (HFSS) software. The designed antenna resonates at 2.8 GHz, 5.8 GHz, and 6.9 GHz for the application requirements of WiMAX, ISM, and Sub-7 GHz wireless communication. The antenna is fabricated as printed circuit board (PCB) format, and its return loss characteristics are measured using a vector network analyser (VNA). The measurement results of the proposed triband antenna are in good agreement with simulated return loss characteristics.

Simerjot Kaur, Andrea Stefanucci, Sameena Shah

Determining industry and product/service codes for a company is an important real-world task and is typically very expensive as it involves manual curation of data about the companies. Building an AI agent that can predict these codes automatically can significantly help reduce costs, and eliminate human biases and errors. However, unavailability of labeled datasets as well as the need for high precision results within the financial domain makes this a challenging problem. In this work, we propose a hierarchical multi-class industry code classifier with a targeted multi-label product/service code classifier leveraging advances in unsupervised representation learning techniques. We demonstrate how a high quality industry and product/service code classification system can be built using extremely limited labeled dataset. We evaluate our approach on a dataset of more than 20,000 companies and achieved a classification accuracy of more than 92\%. Additionally, we also compared our approach with a dataset of 350 manually labeled product/service codes provided by Subject Matter Experts (SMEs) and obtained an accuracy of more than 96\% resulting in real-life adoption within the financial domain.

Qing Yu

Adaptive 5G communication has been accelerated thanks to the catalytic advances in the stable Internet of Things (IoT) which is a growing industry. This essay examines the impact of IoT, describes the evolution of the Internet under beneficial 5G communications, and explores 5G adaptive exchange and coordinating IoT advances. The reproduction evaluation was compatible with the evolution of telephone breathing, and developments in base station rigidity lowered base station energy consumption and club energy under the specification of ensuring communication quality, which means to further reassure compatibility. According to research, in a thick and uniform client course condition, power consumption has dwindled due to the extension of force use, but it has fundamentally improved and the correspondence quality has increased beyond what many would imagine achievable. It can ensure the client’s reasonableness and plan ability to execute, as well as fulfill the rate requirements of a variety of clients. The quick outcomes greatly test the speculative accuracy examination.

Ming-Hao Gong, Yi-Wen Li, Bing Wei et al.

In this paper, a new method of remote calibration in time domain measurement of outfield RCS is proposed. Based on the basic principle of RCS time domain measurement and the characteristics of time domain system, the difference in time and frequency domain measurement is analyzed. The frequency domain remote calibration formula is extended to the time domain by time-frequency transform. Through the analysis of the time domain echo signal of the calibration body in the correction formula and the experimental verification of the calibration sphere echo signal, the influence of the calibration body size and measurement distance on the time domain echo signal and spectrum of the calibration body is given. The typical targets are measured by using the time domain system, and the results of time domain remote calibration, theoretical simulation, and frequency domain measurement are compared. The results show that the error between the time domain remote calibration results and the theoretical simulation results is not more than 1 dB at the −25 dB level. In the outfield measurement, this scheme can fully reflect the advantages of time domain measurement of wide frequency bands and low environmental requirements.

Muhammad Usman Tahir, Umair Rafique, Muhammad Mansoor Ahmed

A 1 × 4 planar fractal array antenna is designed and presented for millimeter-wave 5G applications. The array’s top side is made up of a rhombus-inscribed circular ring fractal patch radiator, while the backside is made up of a square notch-loaded partial ground plane. To achieve high gain, a 1 × 4 corporate feeding network is used to excite the array elements. The designed array has an overall size of 28 × 17.75 mm2. From simulations as well as experimental results, it is observed that the designed array offers a wide impedance bandwidth in the frequency range of 22.8–29.2 GHz. Furthermore, a peak gain of 10.7 dBi with a radiation efficiency of >95% is observed in the operating bandwidth.

Run Liu, Xiaoqing Tian, Boyu Sima et al.

In this paper, a co-caliber structure for dual-polarized dual-band (DPDB) radiation is presented through cross-polarized modes manipulation. After introducing slots and a connection branch into a typical patch antenna, two orthogonal radiation modes with different electrical-length achieved a meandered current mode at the lower frequency and a patch radiation mode at the higher frequency. The radiation patterns at both frequencies are patch-like, benefitting from certain feeding designs. The dual frequencies can be manipulated independently since their working modes are independent. In addition, compared with the traditional patch, the proposed antenna performs a higher aperture efficiency with a miniaturized antenna dimension. The antenna is finally fabricated and measured. The measured results showed that the antenna is evidently cross-polarized working at 10.38 GHz and 14.2 GHz and the gains are 5.57 dB and 6.82 dB, respectively. With the advantages of being compact, easily fabricated, and integrated, the proposed antenna can be potentially utilized in antenna research, polarization manipulation, multichannel communication system, and so on.

Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar et al.

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation transcripts in contact centers to provide business insight. We present two approaches, one entirely based on the transformer-based DistilBERT model, and another that uses a convolutional neural network supplemented with some heuristic rules.

Shah Zeb, Aamir Mahmood, Sunder Ali Khowaja et al.

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, envisioned the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications, e.g., factories-of-the-future, digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zerodefect, and mass customization-based manufacturing solutions. However, the vision requires the merging of cyber-physical worlds through utilizing Industry 5.0 technological enablers, e.g., cognitive cobots, person-centric artificial intelligence (AI), cyberphysical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers in the Industry 5.0 vision, which is the aim of this survey-based tutorial. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, essential for designing the objectives of CINGWNs to fulfill the Industry 5.0 vision requirements. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

Idayat O. Sanusi, K. Nasr, K. Moessner

J. Brunke, Maria T. Johansson, P. Thollander

Ilaria Malanchini, Patrick Agostini, Khurshid Alam et al.

Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facilities. Unlike most previous work that has considered the application of 5G and ML in industrial environment separately, this paper highlights the potential and synergies that result from combining them. The overall vision presented here generates from the KICK project, a collaboration of several partners from the manufacturing and communication industry as well as research institutes. This unprecedented blend of 5G and ML expertise creates a unique perspective on ML-supported industrial communications and their role in facilitating industrial automation. The paper identifies key open industrial challenges that are grouped into four use cases: wireless connectivity and edge-cloud integration, flexibility in network reconfiguration, dynamicity of heterogeneous network services, and mobility of robots and vehicles. Moreover, the paper provides insights into the advantages of ML-based industrial communications and discusses current challenges of data acquisition in real systems.

Nasir Saeed, T. Al-Naffouri, Mohamed-Slim Alouini

Current ground-based transportation systems are subjected to various challenges, including the high cost of infrastructure development, limited land space, and a growing urban population. Therefore, the automotive and aviation industries are collaborating to develop flying cars, also known as electric, vertical, takeoff, and landing aircrafts (eVTOLs). These eVTOLs will allow for rapid and reliable urban and suburban transportation. Safe operation of eVTOLs will require well-developed wireless communication networks; however, existing communication technologies need enhancement in order to provide services to flying cars. We describe several potential innovations that make communication between eVTOLs and the ground feasible. These innovations include three-dimensional cellular networks on-ground, tethered balloons, high-altitude platforms, and satellites.