Optimal Base Station Design with Limited Fronthaul: Massive Bandwidth or\n Massive MIMO?
Bibliographic record
Abstract
To reach a cost-efficient 5G architecture, the use of remote radio heads\nconnected through a fronthaul to baseband controllers is a promising solution.\nHowever, the fronthaul links must support high bit rates as 5G networks are\nprojected to use wide bandwidths and many antennas. Upgrading all of the\nexisting fronthaul connections would be cumbersome, while replacing the remote\nradio head and upgrading the software in the baseband controllers is relatively\nsimple. In this paper, we consider the uplink and seek the answer to the\nquestion: If we have a fixed fronthaul capacity and can deploy any technology\nin the remote radio head, what is the optimal technology? In particular, we\noptimize the number of antennas, quantization bits and bandwidth to maximize\nthe sum rate under a fronthaul capacity constraint. The analytical results\nsuggest that operating with many antennas equipped with low-resolution\nanalog-to-digital converters, while the interplay between number of antennas\nand bandwidth depends on various parameters. The numerical analysis provides\nfurther insights into the design of communication systems with limited\nfronthaul capacity.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".