Bibliographic record
Abstract
Towards a fully connected intelligent digital world, 5G and beyond networks experience a new era of Internet of intelligence with connected people and things. This new era brings challenging demands to the network, such as high spectral efficiency, low-latency, high-reliable communication, and high energy efficiency. One of the major technological breakthroughs to cope with these unprecedented demands is the cell-free (CF) massive multiple-input multiple-output (mMIMO) systems. In CF mMIMO, a large number of distributed access points are connected to a central processing unit, and serve a smaller number of users over the same time-frequency resources. The system has shown a great potential in improving the network performance in various perspectives compared to the co-located mMIMO and conventional small-cell systems. Furthermore, the system can be flexibly integrated with various emerging techniques/technologies for 5G and beyond networks to boost the network performance in different perspectives. Despite the substantial reported theoretical gains of CF mMIMO systems, the full picture of a practical scalable deployment of the system is not clear yet. In this paper, we provide a comprehensive survey of different aspects of the CF mMIMO system from the general system model, the detailed system operation, the limitations towards a practically implemented system to the potential of integrating the system with emerging techniques/technologies. Besides, we provide a number of timely open problems and future research directions to fully exploit the CF mMIMO system potential in delivering the anticipated requirements of future wireless networks.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".