New service and performance requirements
Immersive, highly reliable low-latency, massive, ubiquitous, AI-linked, and sensing-enabled communication scenarios.
Integrating learning track · IMT-2030 and beyond
Emerging 6G & Next-Generation Wireless Technologies
I view 6G as an interconnected research landscape rather than a single technology. My aim is to understand how advanced radio, sensing, intelligence, terrestrial and non-terrestrial coverage, optical and quantum links, security, and sustainability can be modeled and evaluated as parts of one communication system.
Demonstrated foundationMathematical derivation and Monte Carlo validation for multi-user NOMA, BER, throughput, user ordering, power allocation, and SIC
Developing systems viewIMT-2030 scenarios, ISAC, NTN, advanced radio, optical and quantum links, intelligence, resilience, and sustainability
Research landscape
What “future 6G technologies” means in my profile
The phrase describes a broad doctoral direction anchored in communication theory. It does not mean that each technology is already part of my completed research.
New propagation and infrastructure
Sub-THz and advanced radio, massive or distributed arrays, reconfigurable environments, NTN, optical links, and heterogeneous access.
New network intelligence
Model-based and data-driven resource control, semantic or goal-oriented concepts, digital twins, automation, and cross-layer optimization.
New trust and sustainability constraints
Security, privacy, resilience, energy efficiency, responsible intelligence, and measurable social or environmental impact.
Technical foundation
Mathematical tools and technology themes under study
Mathematical toolkit
- Probability, random processes, fading channels, statistical inference, detection, and estimation
- Linear algebra, matrix decompositions, array processing, beamforming, and multi-user signal models
- Information theory, achievable rate, reliability, latency, semantic metrics, and multiobjective tradeoffs
- Convex and non-convex optimization, game and resource-allocation models, and numerical algorithms
- Stochastic geometry, graph models, queues, mobility, coverage, routing, and network reliability
- Machine-learning fundamentals, model-based learning, uncertainty, generalization, and reproducible evaluation
Technology landscape
- Integrated sensing and communication, localization, and environment-aware radio operation
- Non-terrestrial, satellite, aerial, and terrestrial network integration
- Sub-THz links, advanced MIMO, cell-free systems, RIS, and new radio architectures
- AI-native air interfaces and networks, digital twins, automation, and intelligent resource management
- Optical transport, free-space optical systems, quantum communication, QKD, and heterogeneous links
- Trust, privacy, resilience, energy efficiency, spectrum sharing, and sustainable system design
Structured roadmap
Building from standards vision to system models
IMT-2030 scenarios, capabilities, and constraints
Use ITU frameworks to understand why future networks require sensing, ubiquitous coverage, intelligence, resilience, and sustainability.
Advanced radio and heterogeneous connectivity
Study the physical and architectural assumptions behind ISAC, NTN, sub-THz, massive arrays, RIS, optical, and quantum links.
Model-based analysis with selective learning tools
Build interpretable analytical baselines, then examine where optimization or machine learning adds justified value.
Comparable and reproducible performance evidence
Evaluate reliability, rate, sensing accuracy, latency, coverage, energy, complexity, and security under explicit assumptions.
Selected study resources
Standards frameworks, research roadmaps, and recorded talks
These sources organize the wider landscape. Their inclusion does not imply completion or endorsement of every technology they discuss.
Standards and major research organizations are used to separate stable framework documents from more speculative technology claims.
Recommendation ITU-R M.2160
The framework and overall objectives for IMT-2030, including usage scenarios, capabilities, and design principles.
International Telecommunication UnionReport ITU-R M.2516
A broad review of terrestrial IMT technology trends toward 2030 and beyond, including AI-native communication, ISAC, advanced radio, and NTN interconnection.
International Telecommunication Union6G Research Visions
The 6G Flagship collection covering networking, RF, machine learning, sensing, security, remote connectivity, resilience, and societal drivers.
6G FlagshipRoadmap to 6G
A Next G Alliance perspective spanning technical, spectrum, application, sustainability, trust, and standardization priorities.
Next G Alliance6G Flagship Video Library
Recorded research talks, webinars, interviews, and technical discussions from the University of Oulu's 6G Flagship program.
6G Flagship on YouTubeKey Drivers and Research Challenges for 6G
An early but influential white paper organizing societal drivers, applications, metrics, spectrum, networking, and hardware challenges.
6G FlagshipBridge from completed work
A credible base for broader 6G research
My completed work is narrower than the full 6G landscape, but it demonstrates the analytical behavior I want to carry into doctoral research.
Generalized multi-user modeling My thesis moved from specific NOMA cases to n-user analytical frameworks, an approach relevant to scalable future-system models.
Mathematics plus simulation BER and throughput derivations were evaluated through Monte Carlo simulation, providing a repeatable pattern for testing new hypotheses.
Cross-domain technical exposure Signal-processing coursework, RF and HFSS work, UAV simulation, MATLAB, Python, and C++ support future learning across physical and network layers.
Standards lens
Following the IMT-2030 process
ITU IMT-2030 portal
The current framework, timeline, technology reports, capabilities, and radio-interface development process in one official location.
Follow IMT-2030Framework versus candidate technologies
I treat M.2160 as a stable requirements-level anchor while evaluating individual enablers through primary literature and explicit system assumptions.
Read Recommendation M.2160