A Novel Distributed Antenna Access Architecture for 5G Indoor Service Provisioning
Bibliographic record
Abstract
In order to overcome the non-line-of-sight nature of the indoor environment so as to achieve a cost-effective solution of 5G indoor service provisioning, distribution of antenna units in indoor chambers is the most straightforward solution. This paper investigates a novel distributed antenna access architecture that allows the antenna units to be distributed over a wide geographical area via multi-pair LAN cables. The proposed architecture supports simultaneous transmission of multiple intermediate frequency signals between the remote radio unit and each distributed antenna unit. To explore the capacity of the LAN cables, we introduce a real-time multi-pair air-to-cable (MP-A2C) scheduler that allows for a graceful mapping between the radio signal spectrum and sub-channels of the cable twisted pairs, as well as power shaping of each sub-channel signal. We will first provide the problem formulation of the optimal MP-A2C process, which is nonetheless non-convex and computationally intractable. To achieve real-time solution of the problem, we reformulate the problem into two sub-problems that can be solved in a divide-and-conquer manner. Extensive numerical results show that the formulated MP-A2C problem can easily lead to quasi-optimal schedules.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".