Adaptive in-band self-backhauling for full-duplex small cells
Bibliographic record
Abstract
Recent advancements in the self-interference (SI) cancellation capability of low-power wireless devices pave the way of implementing full-duplex (FD) self-backhauling in small-cell networks. FD self-backhauling allows exploiting the radio spectrum used by the radio access network (RAN) for backhaul links as well as access links concurrently. This paper characterizes the performance of FD self-backhauling for both downlink and uplink transmissions in a two-tier macrocell-small cell network and compares it against half-duplex (HD) selfbackhauling. The performance of small cell base stations (SBSs) with adaptive FD scheme that switches between HD and FD modes depending on the network parameters such as the distance of backhaul link (i.e., link between the macrocell base station and SBS), SI cancellation value at the SBS, and the distance of the served user from the SBS is then numerically analyzed for two different user selection schemes used by the SBS. Numerical results show the usefulness of adaptive FD self-backhauling over FD self-backhauling, especially in fairness-constrained scheduling schemes.
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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) | 0.000 | 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) | 0.001 | 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 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".