Hasil untuk "Telecommunication"

Chi-Sheng Chen, Samuel Yen-Chi Chen, Yun-Cheng Tsai

In this study, we evaluate the performance of classical and quantum-inspired sequential models in forecasting univariate time series of incoming SMS activity (SMS-in) using the Milan Telecommunication Activity Dataset. Due to data completeness limitations, we focus exclusively on the SMS-in signal for each spatial grid cell. We compare five models, LSTM (baseline), Quantum LSTM (QLSTM), Quantum Adaptive Self-Attention (QASA), Quantum Receptance Weighted Key-Value (QRWKV), and Quantum Fast Weight Programmers (QFWP), under varying input sequence lengths (4, 8, 12, 16, 32 and 64). All models are trained to predict the next 10-minute SMS-in value based solely on historical values within a given sequence window. Our findings indicate that different models exhibit varying sensitivities to sequence length, suggesting that quantum enhancements are not universally advantageous. Rather, the effectiveness of quantum modules is highly dependent on the specific task and architectural design, reflecting inherent trade-offs among model size, parameterization strategies, and temporal modeling capabilities.

Xuhang Chen, Bo Lv, Mengqian Wang et al.

Customer churn prediction in the telecommunications sector represents a critical business intelligence task that has evolved from subjective human assessment to sophisticated algorithmic approaches. In this work, we present a comprehensive framework for telecommunications churn prediction leveraging deep neural networks. Through systematic problem formulation, rigorous dataset analysis, and careful feature engineering, we develop a model that captures complex patterns in customer behavior indicative of potential churn. We conduct extensive empirical evaluations across multiple performance metrics, demonstrating that our proposed neural architecture achieves significant improvements over existing baseline methods. Our approach not only advances the state-of-the-art in churn prediction accuracy but also provides interpretable insights into the key factors driving customer attrition in telecommunications services.

Casper A. Breum, Xueshi Guo, Mikkel V. Larsen et al.

Optical non-Gaussian states hold great promise as a pivotal resource for advanced optical quantum information processing and fault-tolerant long-distance quantum communication. Establishing their faithful transmission in a real-world communication channel, therefore, marks an important milestone. In this study, we experimentally demonstrate the distribution of such non-Gaussian states in a functioning telecommunication channel that connects separate buildings within the DTU campus premises. We send photon-subtracted squeezed states, exhibiting pronounced Wigner negativity, through 300 m of deployed optical fibers to a distant building. Using quantum homodyne tomography, we fully characterize the states upon arrival. Our results show the survival of the Wigner function negativity after transmission when correcting for detection losses, indicating that the established link can potentially facilitate the violation of Bell's inequality and enable quantum steering. This achievement not only validates the practical feasibility of distributing non-Gaussian states in real-world settings, but also provides an exciting impetus towards realizing fully coherent quantum networks for high-dimensional, continuous-variable quantum information processing.

Amin Zamani, Gabriel Sanderson, Lu Zhang et al.

The growing demand for more efficient data transmission has made nanoscale high-throughput all-optical switching a critical requirement in modern telecommunication systems. Metasurface-based platforms offer unique advantages because of their compact design, energy efficiency, and the ability to precisely manipulate light at the subwavelength scale, in a contact-less fashion. However, achieving both high transmission modulation and low optical loss in the telecom band remains a challenge. This study develops monolithic and hybrid metasurfaces based on the phase change material antimony trisulfide (Sb$_2$S$_3$) to address this limitation. First, we demonstrate the capability of Sb$_2$S$_3$ to offer up to ~91 percent modulation, even with a magnetic dipole - a low-Q resonance. It lifts the requirement for complex precisely fabricated metasurfaces, a long-standing limitation in the community for all optical switching. Furthermore, with the most straightforward hybridisation approach, i.e. depositing a thin film of silicon, we improved the simulated modulation depth to 99 percent. Experimentally, over 80 percent modulation was achieved for both hybrid and monolithic structures, with nearly 2-fold less power required for switching in the hybrid design whilst maintaining high modulation depth. This performance results from the significant refractive index tunability of Sb$_2$S$_3$ and its intrinsically low optical loss (k < 10^{-4}) in the telecom band, further enhanced by silicon integration. The demonstrated metasurfaces offer an effective and scalable approach for all-optical light modulation with strong potential for integration into CMOS-compatible photonic circuits and next-generation telecommunications systems.

Fabrice von Chamier, Joscha Hanel, Chris Müller et al.

Interconnected quantum devices are the building blocks of quantum networks, where state transduction plays a central role. The frequency conversion of photons into the telecommunication C-band is decisive in taking advantage of current low-loss transmission lines. Here, we report the difference frequency conversion of $637.2$ nm fluorescent light from a cluster of NV centers in diamond to tunable wavelengths between $1559.6$ nm and $1565.2$ nm. In order to avoid detrimental noise from spontaneous emissions, we use a two-step conversion device based on a single-pumped periodic poled lithium niobate waveguide. We observed a total external (internal) conversion efficiency of $3.0\pm0.1$ ($20.5\pm0.8$) $\%$ with a noise rate of $2.4\pm0.8$ ($16\pm5$) cps/GHz.

Agus Wantoro, Wahyu Caesarendra, Admi Syarif et al.

Early detection of thyroid cancer recurrence is a crucial factor in patient survival and treatment effectiveness. Misdetection results in disease severity, high cost, recovery time, and decreased service quality. In addition, the main challenges in developing a Machine Learning (ML)-based detection decision support system are class imbalance in medical data and high feature dimensions that can affect model accuracy and efficiency. This study proposes a feature selection-based approach and class imbalance handling to improve the performance of early detection of Thyroid cancer. Several feature selection techniques, such as Information Gain (IG), Gain Ratio (GR), Gini Decrease (GD), and Chi-Square (CS), can select features based on weighted ranking. In addition, to overcome the imbalanced class distribution, we use the Synthetic Minority Over-Sampling Technique (SMOTE). ML classification models such as k-NN, Tree, SVM, Naive Bayes, AdaBoost, Neural Network (NN), and Logistic Regression (LR) are tested and evaluated based on a confusion matrix, including accuracy, precision, recall, time, and log loss. Experimental results show that the combination of imbalanced class handling strategies significantly improves the prediction performance of ML algorithms. In addition, we found that the combination of CS+NN feature selection techniques consistently showed optimal performance. This study emphasizes the importance of data pre-processing and proper algorithm selection in the development of a machine learning-based thyroid cancer detection system.

Blessed Guda, Gabrial Zencha Ashungafac, Lawrence Francis et al.

Large Language models (LLMs) have brought about substantial advancements in the field of Question Answering (QA) systems. These models do remarkably well in addressing intricate inquiries in a variety of disciplines. However, because of domain-specific vocabulary, complex technological concepts, and the requirement for exact responses applying LLMs to specialized sectors like telecommunications presents additional obstacles. GPT-3.5 has been used in recent work, to obtain noteworthy accuracy for telecom-related questions in a Retrieval Augmented Generation (RAG) framework. Notwithstanding these developments, the practical use of models such as GPT-3.5 is restricted by their proprietary nature and high computing demands. This paper introduces QMOS, an innovative approach which uses a Question-Masked loss and Option Shuffling trick to enhance the performance of LLMs in answering Multiple-Choice Questions in the telecommunications domain. Our focus was on using opensource, smaller language models (Phi-2 and Falcon-7B) within an enhanced RAG framework. Our multi-faceted approach involves several enhancements to the whole LLM-RAG pipeline of finetuning, retrieval, prompt engineering and inference. Our approaches significantly outperform existing results, achieving accuracy improvements from baselines of 24.70% to 49.30% with Falcon-7B and from 42.07% to 84.65% with Phi-2.

Qiang Zeng, Huihong Yuan, Haoyang Wang et al.

Nonlocal correlation represents the key feature of quantum mechanics, and is an exploitable resource in quantum information processing. However, the loophole issues and the associated applicability compromises hamper the practical applications. We report the first time-bin entangled detection-loophole-free steering nonlocality demonstration in a fully chip-fiber telecommunication system, with an ultra-fast measurement switching rate (1.25~GHz). In this endeavor, we propose the phase-encoding measurement scheme to adapt the system to time-bin degree of freedom, and design and fabricate a low-loss silicon chip for efficient entanglement generation. An asymmetric configuration is introduced to mimic the active measurement implementation at the steering party thus bypassing the phase modulation loss. Consequently, we build a fiber-optic setup that can overcome the detection efficiency required by conclusive quantum steering with multiple actively switched measurement settings. Our setup presents an immediate platform for exploring applications based on steering nonlocality, especially for quantum communication.

Mirko Pittaluga, Yuen San Lo, Adam Brzosko et al.

Quantum communications harness quantum phenomena like superposition and entanglement to enhance information transfer between remote nodes. Coherent quantum communications, essential for phase-based quantum internet architecture, require optical coherence among nodes and typically involve single-photon interference. Challenges like preserving optical coherence and integrating advanced single-photon detectors have impeded their deployment in existing telecommunication networks. This study introduces innovative approaches to the architecture and techniques supporting coherent quantum communications, marking their first successful integration within a commercial telecom infrastructure between Frankfurt and Kehl, Germany. Employing the Twin Field Quantum Key Distribution protocol, we achieved encryption key distribution at 110 bit/s over 254 km. This system features measurement-device-independent properties and non-cryogenically cooled detectors, and represents the first effective quantum repeater implementation on telecom infrastructure, the longest practical quantum key distribution deployment to date, and validates the feasibility of a phase-based quantum internet architecture.

Domenico Ribezzo, Roberto Salazar, Jakub Czartowski et al.

In a world where Quantum Networks are rapidly becoming a reality, the development of the Quantum Internet is gaining increasing interest. Nevertheless, modern quantum networks are still in the early stages of development and have limited capacity to distribute resources among different users -- a constraint that needs to be taken into account. In this work we aim to investigate these constraints, using a novel setup for implementing Quantum Random Access Codes (QRACs), communication protocols known for their quantum advantage over their classical counterparts and semi-device-independent self-testing applications. Our QRAC states, made for the first time using weak coherent pulses instead of entangled single photons, allow us to experimentally test our encoding and decoding strategy from the resource allocation perspective. Moreover, by emulating a coexistent classical communication, we test the resilience of our implementation in presence of noise. The achieved results represent a significant milestone both for theoretical studies of quantum resource allocation and for the implementation of quantum infrastructures capable of coexisting with regular telecommunication networks.

Semih Önüt, Selin Soner Kara, Elif Işık

Junbing LI, Youjun ZENG, Xiaoping ZENG et al.

Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods.To address this issue, a model-data dual-driven bidirectional gated recurrent unit (BiGRU) network for short-term prediction of MUF was proposed.On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network.On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN.The simulation results show that the proposed network has reduced average root mean squared error (RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.

Tejinder Singh, Gwendolyn Hummel, Mohammad Vaseem et al.

Chalcogenide Phase Change Materials (PCM) and metal insulator transition (MIT) materials are a group of materials that are capable of switching between low resistance and high resistance states. These emerging materials have been widely used in optical storage media and memory devices. Over the past recent years, there have been interests in exploiting the PCM and MIT materials, especially germanium antimony telluride (GST) alloys and vanadium dioxide (VO₂), for radio frequency (RF) applications. The PCM and MIT-based RF devices are expected to bridge the gap between semiconductor switches and microelectromechanical system (MEMS) switches as they combine the low insertion loss performance of MEMS technology and the small size and reliability performance of semiconductor technology. This article presents an overview of the PCM and MIT materials for RF circuits and discusses the recent advancements in reconfigurable millimeter-wave (mmWave) devices based on PCM and MIT materials in depth.

Klaus Uhlenbruck, Peter Rodriguez, J. Doh et al.

Dhanalekshmi Prasad Yedurkar, Shilpa P. Metkar, Fadi Al-Turjman et al.

A novel approach for multichannel epilepsy seizure classification which will help to automatically locate seizure activity present in the focal brain region was proposed. This paper suggested an Internet of Things (IoT) framework based on a smart phone by utilizing a novel feature termed multiresolution critical spectral verge (MCSV), based on frequency-derived information for epileptic seizure classification which was optimized using a flower pollination algorithm (FPA). A wireless sensor technology (WSN) was utilized to record the electroencephalography (EEG) signal of epileptic patients. Next, the EEG signal was pre-processed utilizing a multiresolution-based adaptive filtering (MRAF) method. Then, the maximal frequency point at which the power spectral density (PSD) of each EEG segment was greater than the average spectral power of the corresponding frequency band was computed. This point was further optimized to extract a point termed as critical spectral verge (CSV) to extract the exact high frequency oscillations representing the actual seizure activity present in the EEG signal. Next, a support vector machine (SVM) classifier was used for channel-wise classification of the seizure and non-seizure regions using CSV as a feature. This process of classification using the CSV feature extracted from the MRAF output is referred to as the MCSV approach. As a final step, cloud-based services were employed to analyze the EEG information from the subject’s smart phone. An exhaustive analysis was undertaken to assess the performance of the MCSV approach for two datasets. The presented approach showed an improved performance with a 93.83% average sensitivity, a 97.94% average specificity, a 97.38% average accuracy with the SVM classifier, and a 95.89% average detection rate as compared with other state-of-the-art studies such as deep learning. The methods presented in the literature were unable to precisely localize the origination of the seizure activity in the brain region and reported a low seizure detection rate. This work introduced an optimized CSV feature which was effectively used for multichannel seizure classification and localization of seizure origination. The proposed MCSV approach will help diagnose epileptic behavior from multichannel EEG signals which will be extremely useful for neuro-experts to analyze seizure details from different regions of the brain.

Hang Wang, Yunfeng Chen, Rui Min et al.

Distributed acoustic sensing (DAS) is an emerging technology for recording vibration signals via the optical fibers buried in subsurface conduits. Its relatively easy-to-deploy and high spatial and temporal sampling characteristics make DAS an appealing tool to record seismic wavefields at higher quantity and quality than traditional geophones. Considering that the usage of optical fibers in the urban environment has drawn relatively less attention aside from its functionality as a telecommunication cable, we examine its ability to record seismic signals and investigate its preliminary application in city traffic monitoring. To solve the problems that DAS signals are prone to a variety of environmental noise and are generally of weak amplitude compared to noise, we propose a fast workflow for real-time DAS data processing, which can enhance the detection of regular car signals and suppress the other components. We conduct a DAS experiment in Hangzhou, China, a typical metropolitan area that can provide us with a rich data library to validate our DAS data-processing workflow. The well-processed data enable us to extract their slope and coherency attributes that can provide an estimate of real traffic situations. The one-minute (with video validations) and 24 h statistics of these attributes show that the speed and volume of car flow are well correlated demonstrates the robustness of the proposed data processing workflow and great potential of DAS for city traffic monitoring with high precision and convenience. However, challenges also exist in view that all the attributes are statistically analyzed based on the behaviors of a large number of cars, which is meaningful but lacking in precision. Therefore, we suggest developing more quantitative processing and analyzing methods to provide precise information on individual cars in future works.

Ye Ouyang, Lilei Wang, Aidong Yang et al.

It has been an exciting journey since the mobile communications and artificial intelligence were conceived 37 years and 64 years ago. While both fields evolved independently and profoundly changed communications and computing industries, the rapid convergence of 5G and deep learning is beginning to significantly transform the core communication infrastructure, network management and vertical applications. The paper first outlines the individual roadmaps of mobile communications and artificial intelligence in the early stage, with a concentration to review the era from 3G to 5G when AI and mobile communications started to converge. With regard to telecommunications artificial intelligence, the paper further introduces in detail the progress of artificial intelligence in the ecosystem of mobile communications. The paper then summarizes the classifications of AI in telecom ecosystems along with its evolution paths specified by various international telecommunications standardization bodies. Towards the next decade, the paper forecasts the prospective roadmap of telecommunications artificial intelligence. In line with 3GPP and ITU-R timeline of 5G & 6G, the paper further explores the network intelligence following 3GPP and ORAN routes respectively, experience and intention driven network management and operation, network AI signalling system, intelligent middle-office based BSS, intelligent customer experience management and policy control driven by BSS and OSS convergence, evolution from SLA to ELA, and intelligent private network for verticals. The paper is concluded with the vision that AI will reshape the future B5G or 6G landscape and we need pivot our R&D, standardizations, and ecosystem to fully take the unprecedented opportunities.

Jinyin CHEN, Wenchang SHANGGUAN, Jingjing ZHANG et al.

For the problem of poor performance of exciting membership inference attack (MIA) when facing the transfer learning model that is generalized, the MIA for the transfer learning model that is generalized was first systematically studied, the anomaly detection was designed to obtain vulnerable data samples, and MIA was carried out against individual samples.Finally, the proposed method was tested on four image data sets, which shows that the proposed MIA has great attack performance.For example, on the Flowers102 classifier migrated from VGG16 (pretraining with Caltech101), the proposed MIA achieves 83.15% precision, which reveals that in the environment of transfer learning, even without access to the teacher model, the MIA for the teacher model can be achieved by visiting the student model.