Next-Generation Wireless Intelligence Communications Laboratory
Research Field
Anal Paul (Member, IEEE) is an Assistant Professor in the Department of Computer Science and Engineering at Yuan Ze University, Taiwan. He received the B.Tech. degree from the Government College of Engineering and Ceramic Technology, India, in 2008, the M.E. degree from Jadavpur University, India, in 2010, and the Ph.D. degree from the Indian Institute of Engineering Science and Technology, Shibpur, India, in 2021.
From July to December 2022, he was a Postdoctoral Researcher with the Department of Information and Communication Engineering, Yeungnam University, South Korea. From January 2023 to January 2025, he was a Postdoctoral Researcher at National Sun Yat-sen University (NSYSU), Taiwan. His research interests include quantum machine learning, federated learning, deep reinforcement learning, RIS, NOMA, ISAC, and digital twins for resource allocation in B5G/6G wireless networks.
Next-Generation Wireless Intelligence Communications (Next-WinC) Laboratory is a dynamic research group in the Computer Science and Engineering Department at Yuan-Ze University, Taiwan. Our lab is dedicated to advancing the frontiers of wireless communication, with a particular emphasis on beyond 5G and 6G technologies. We strive to address the growing demands for seamless, high-speed, and ubiquitous connectivity in modern society by bridging academic research and industrial applications.
At Next-WinC, our research spans a range of cutting-edge topics, including:
- Next-Generation Multiple Access: Pioneering techniques to handle rapidly increasing connectivity demands.
- UAV-Assisted Wireless Networking: Exploring drone-based solutions for extended coverage and improved network performance.
- Reconfigurable Intelligent Surfaces (RIS): Enhancing communication reliability and efficiency in both V2V and V2I scenarios.
- AI & Quantum Machine Learning Integration: Incorporating classical machine learning, deep learning, and advanced Quantum machine learning to drive intelligent wireless systems.
- Innovative Network Solutions: Our overarching mission is to develop cutting-edge technologies that address the evolving challenges of modern communication networks.
- Visvesvaraya PhD Fellowship Awardee (Awarded by the Ministry of Electronics and Information Technology, Government of India)
- GATE Qualified (Graduate Aptitude Test in Engineering – India)
- UGC-NET Qualified (National Eligibility Test – India)
Doctor of Philosophy (Ph.D.)
Department of Information Technology
Indian Institute of Engineering Science and Technology, Shibpur
Year: 2015 – 2021
Thesis Title: Energy-Efficient Spectrum Sensing and Data Transmission in Multihop Cognitive Radio Networks
Master of Engineering (M.E.)
Software Engineering, Department of Information Technology
Jadavpur University, India
Year: 2008 – 2010
Bachelor of Technology (B.Tech)
Department of Information Technology
Government College of Engineering and Ceramic Technology, India
Year: 2004 – 2008
Job Description
- Perform literature surveys, curate datasets, and run large-scale simulations on lab servers.
- Analyze results, visualize key performance metrics, and assist in drafting technical reports, slides, and journal/conference manuscripts.
- Present weekly progress, collaborate with graduate students, and contribute ideas to shape ongoing projects.
Preferred Intern Education Level
Any of the following applicants are welcome:
- Final-year B.Tech./B.E. students
- Master’s students
- Early-stage PhD candidates
— in Computer Science, Electrical/Communications Engineering, Applied Mathematics, or closely related disciplines.
Skill sets or Qualities
- Proficiency in Python programming; experience with machine-learning frameworks (TensorFlow or PyTorch).
- Solid understanding of wireless communications fundamentals (channel models, multiple access, network protocols).
- Familiarity with reinforcement learning, federated learning, or digital-twin concepts is a plus.
- Strong analytical and problem-solving abilities, self-motivated attitude, and effective English communication skills.
Job Description
- Implement and test algorithms using Qiskit, Cirq, or PennyLane on cloud simulators and, when available, NISQ hardware back-ends; keep clean, well-commented Python repositories.
- Benchmark against classical baselines (e.g., deep neural networks in PyTorch/TensorFlow) under realistic 6G channel models; analyze latency, reliability, and energy-efficiency trade-offs.
- Visualize results, write concise technical briefs, and prepare figures for journal or conference submissions; present weekly progress updates to the research group.
- Contribute to grant reports and collaborative papers, gaining hands-on authorship experience in a fast-moving international research environment.
Preferred Intern Education Level
Final-year B.Tech./B.E. students, Master’s students, or early-stage PhD candidates in Computer Science, Electrical/Communications Engineering, Applied Mathematics, Physics, or closely related disciplines.
Skill sets or Qualities
- Strong Python programming skills
- Familiarity with quantum-computing frameworks (Qiskit, Cirq, PennyLane) and ML libraries (PyTorch, TensorFlow)
- Solid grounding in wireless communications fundamentals
- Analytical mindset, self-motivation, and clear English communication