6G Cellular Networks: Mapping the Landscape for the IMT-2030 Framework
Bibliographic record
Abstract
The IMT-2030 framework provides the vision and conceptual foundation for the next-generation of mobile broadband systems, colloquially known as Sixth-Generation (6G) cellular networks. Academic circles, industry players, and Standard Developing Organizations (SDOs) are already engaged in early standardization discussions for the system, providing key insights for future technical specifications. In this context, a structured thematic review of literature contributions aligned with IMT-2030 is essential to inform the discussions and assist collaboration among 6G stakeholders—including scholars, professionals, regulators, and SDO officials. This article adopts a semi-systematic methodology to identify, analyze, and synthesize 6G literature across five core thematic areas: (1) 6G Vision, (2) Use Cases, Performance Requirements, and Architectural Trends (3) Enabling Technologies, (4) Impact on Vertical Sectors, and (5) the 6G Research Frontier. The core themes follow an evolution-oriented structure that mirrors the transition from 5G to 6G, in line with the design principles, use cases, technical capabilities, and technological trends outlined for IMT-2030. To ensure coverage of well-established contributions, the article screens literature published between 2019 and 2025 using IEEE Xplore and Scopus databases, prioritizing highly cited papers, seminal white papers, and early SDO documentation aligned with the IMT-2030 framework. This approach balances breadth and depth, allowing for a representative overview of the field. By combining structured screening with thematic synthesis, this article delivers a concise yet comprehensive account of 6G literature for both specialists and generalists engaged in shaping future standards and advancing 6G research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".