A Survey on 6G Networks: Vision, Requirements, Architecture, Technologies and Challenges
Bibliographic record
Abstract
Our society is increasingly dependent on digitization. For example, different types of physical and virtual objects are connected to the Internet of Things, all services are digitized, and the number of connected devices continues to grow, which leads to the exchange of large amounts of data. The current communication network 5G cannot meet the needs of the future. Therefore, the demand for high-speed mobile communications is essential to better prepare for the arrival of new services and emerging applications. Namely, extended reality, holographic communication, sensory internet, human digital twin, smart city and industry, etc. These new use cases are applied in many different areas. For example, health, autonomous transportation, climate, network security, etc. Therefore, the research of the new generation network 6G has begun to bear the limits of 5G and deal with new challenges. This paper conducts a related investigation on the sixth-generation communication network. First, the vision, requirements, and expected application scenarios of the 6G network are introduced. Then, it describes the integration of intelligent architecture and space, air, ground, and sea networks. Subsequently, the most important potential key technologies needed for the future sixth-generation were exposed and analyzed. Finally, the main research activities carried out are introduced.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".