Capacity planning for 5G packet-based front-haul
Bibliographic record
Abstract
Packet switched transport technologies have been suggested to be used in 5G front-haul networks. Front-haul bandwidth and packet latency are two major challenges to be addressed in these networks. This paper focuses on capacity planning for packet-based front-haul technologies considering the latency requirements of the 5G front-haul flows. More specifically, we answer three main questions for Constant Bit Rate (CBR) and Variable Bit Rate (VBR) packet front-haul networks: First, is statistical multiplexing gain always achievable in packet front-haul networks? Second, what is the minimum bandwidth required to accommodate a set of front-haul flows on a front-haul link? Third, what is the proper bandwidth allocation and the scheduling algorithm among the packets of different antennas to guarantee the latency requirements of the flows? It is shown that for CBR traffic no statistical multiplexing gain is achievable and the Earliest Deadline First (EDF) is the optimal packet scheduler. For VBR traffic, statistical multiplexing gain is achievable only when the latency requirement of at least one flow is greater than the packet inter-arrival time. The optimal bandwidth allocation is also determined for a Weighted Fair Queueing (WFQ) scheduler. Finally, simulation is used to validate the theoretical results for both CBR and VBR front-haul traffic.
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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 | 0.000 | 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".