Selected work

Representative Papers

LDGM-Based Quantum Codes for Fault-Tolerant Quantum Computation

Jul 16, 2026

This work addresses the challenge in fault-tolerant quantum computing of simultaneously achieving high error-correction performance and low stabilizer weight. By leveraging low-density generator matrix (LDGM) codes and the Calderbank–Shor–Steane (CSS) construction, the authors design a new class of quantum error-correcting codes. Through flexible row operations, the code rate is efficiently tuned, while message-passing iterative decoding on graphs—combined with discrete density evolution analysis—enables significantly reduced stabilizer generator weights without compromising error-correction capability. The resulting quantum codes exhibit outstanding performance under the depolarizing channel, offering both low decoding complexity and high practicality. This approach provides an efficient and scalable coding solution for fault-tolerant quantum computation.

0 citationsRead paper

An Efficient Machine Learning-based Framework for Detection and Prevention of Frauds in Telecom Networks

May 17, 2026

This study addresses the growing threat of telecommunication fraud, which severely undermines network reliability and incurs substantial economic losses. To combat this issue, the authors propose an efficient fraud detection framework leveraging call detail record (CDR) data. The approach employs Min-Max normalization and SMOTE oversampling to handle high-dimensional, imbalanced datasets, followed by a systematic evaluation of multiple machine learning and deep learning models—including Random Forest, XGBoost, DBSCAN, K-means, and RoBERTa—on their detection performance. Experimental results demonstrate that Random Forest achieves exceptional performance with 99.9% accuracy, precision, recall, and F1-score, significantly outperforming all other methods. This outcome confirms the framework’s effectiveness and practicality in enabling high-precision, low-false-positive fraud identification.

0 citationsRead paper

Sustainability in Telecom: Energy-Efficient Networks and Circular Economy Models to Reduce Carbon Footprints and Increase Efficiency

May 15, 2026

This study addresses the pressing challenges of high energy consumption and carbon emissions driven by 5G deployment and surging data traffic, proposing the first integrated framework for telecommunications sustainability that unifies green networking with circular economy principles. The approach holistically optimizes technology, supply chains, and policy: it enhances energy efficiency through dynamic base station sleeping, AI-driven traffic scheduling, and renewable energy integration, while simultaneously promoting equipment reuse, lifespan extension, and regulated e-waste management to reduce resource consumption. Validated across multiple leading global telecom operators, the framework demonstrates significant reductions in both energy use and operational carbon emissions, alongside tangible cost savings and enhanced brand value, thereby offering the industry a systematic pathway toward decarbonization.

0 citationsRead paper

IoT and Massive Connectivity: Massive MIMO Optimization for IoT Connectivity in 5G and Beyond Networks

May 15, 2026

This work addresses the challenges of pilot contamination, user scheduling, and energy efficiency bottlenecks in dense Internet-of-Things (IoT) scenarios within 5G and beyond networks. To tackle these issues, the study proposes a synergistic optimization framework integrating machine learning–driven intelligent resource allocation, advanced channel estimation, and hybrid beamforming. This approach significantly enhances the capacity, reduces latency, and improves energy efficiency of massive MIMO systems. Furthermore, the research prospectively investigates emerging paradigms such as cell-free architectures, intelligent reflecting surfaces, and AI-native network orchestration. Simulation results elucidate the fundamental trade-offs among capacity, latency, and energy efficiency, identifying optimal operating points tailored to diverse IoT applications and thereby substantially improving overall system performance.

0 citationsRead paper

Recent publications

Latest Papers

LDGM-Based Quantum Codes for Fault-Tolerant Quantum Computation

Jul 16, 2026

This work addresses the challenge in fault-tolerant quantum computing of simultaneously achieving high error-correction performance and low stabilizer weight. By leveraging low-density generator matrix (LDGM) codes and the Calderbank–Shor–Steane (CSS) construction, the authors design a new class of quantum error-correcting codes. Through flexible row operations, the code rate is efficiently tuned, while message-passing iterative decoding on graphs—combined with discrete density evolution analysis—enables significantly reduced stabilizer generator weights without compromising error-correction capability. The resulting quantum codes exhibit outstanding performance under the depolarizing channel, offering both low decoding complexity and high practicality. This approach provides an efficient and scalable coding solution for fault-tolerant quantum computation.

0 citationsRead paper

An Efficient Machine Learning-based Framework for Detection and Prevention of Frauds in Telecom Networks

May 17, 2026

This study addresses the growing threat of telecommunication fraud, which severely undermines network reliability and incurs substantial economic losses. To combat this issue, the authors propose an efficient fraud detection framework leveraging call detail record (CDR) data. The approach employs Min-Max normalization and SMOTE oversampling to handle high-dimensional, imbalanced datasets, followed by a systematic evaluation of multiple machine learning and deep learning models—including Random Forest, XGBoost, DBSCAN, K-means, and RoBERTa—on their detection performance. Experimental results demonstrate that Random Forest achieves exceptional performance with 99.9% accuracy, precision, recall, and F1-score, significantly outperforming all other methods. This outcome confirms the framework’s effectiveness and practicality in enabling high-precision, low-false-positive fraud identification.

0 citationsRead paper

Sustainability in Telecom: Energy-Efficient Networks and Circular Economy Models to Reduce Carbon Footprints and Increase Efficiency

May 15, 2026

This study addresses the pressing challenges of high energy consumption and carbon emissions driven by 5G deployment and surging data traffic, proposing the first integrated framework for telecommunications sustainability that unifies green networking with circular economy principles. The approach holistically optimizes technology, supply chains, and policy: it enhances energy efficiency through dynamic base station sleeping, AI-driven traffic scheduling, and renewable energy integration, while simultaneously promoting equipment reuse, lifespan extension, and regulated e-waste management to reduce resource consumption. Validated across multiple leading global telecom operators, the framework demonstrates significant reductions in both energy use and operational carbon emissions, alongside tangible cost savings and enhanced brand value, thereby offering the industry a systematic pathway toward decarbonization.

0 citationsRead paper

IoT and Massive Connectivity: Massive MIMO Optimization for IoT Connectivity in 5G and Beyond Networks

May 15, 2026

This work addresses the challenges of pilot contamination, user scheduling, and energy efficiency bottlenecks in dense Internet-of-Things (IoT) scenarios within 5G and beyond networks. To tackle these issues, the study proposes a synergistic optimization framework integrating machine learning–driven intelligent resource allocation, advanced channel estimation, and hybrid beamforming. This approach significantly enhances the capacity, reduces latency, and improves energy efficiency of massive MIMO systems. Furthermore, the research prospectively investigates emerging paradigms such as cell-free architectures, intelligent reflecting surfaces, and AI-native network orchestration. Simulation results elucidate the fundamental trade-offs among capacity, latency, and energy efficiency, identifying optimal operating points tailored to diverse IoT applications and thereby substantially improving overall system performance.

0 citationsRead paper