Why are relays not always good for you? Performance of different relay deployment configurations in a heterogeneous network
Bibliographic record
Abstract
In this paper, we study three scenarios of small-cell deployment in a heterogeneous network comprising a macro base station (MBS) and a set of small cells. The first scenario corresponds to wired backhauling and the remaining two scenarios correspond to two different ways of wireless backhauling. In one of the scenarios of wireless backhauling, the backhaul links have to compete with the user links for LTE channel resources (user-band relay scenario). In the other, an additional (mmWave) band is available exclusively for operating the backhaul links (dedicated-band relay scenario). For each of the scenarios, several different configurations based on channel allocation and node capabilities are considered. We formulate an optimization framework to model, dimension and evaluate all of these configurations. For user-band relay scenario, our results show that some configurations offer either negative or negligible throughput gains over the MBS-only case. By noting that any relay deployment's performance is upper-bounded by wired backhauling with sufficiently large capacity, the results also show that some other configurations offer very good throughput gains, with values very close to the upper-bound. The results highlight the importance of the right choice of configuration to justify the deployment of user-band relay nodes. Further, our results show that, for dedicated-band relay scenario, a small fraction of a typical mmWave bandwidth suffices to yield performance very close to the upper-bound.
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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".