Massive MIMO-Enabled Wireless Backhauls 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 the use of conventional radio access network (RAN) spectrum for backhaul as well as access links concurrently. In this paper, we model and analyze massive MIMO- enabled wireless backhaul networks that are composed of a mixture of small cells, configured either in in-band or out-of-band backhaul mode with a certain probability. We consider a hierarchical network structure to model these networks and characterize the downlink coverage probability of a small cell base station (SBS) for both the in-band and out-of-band backhaul modes. The impact of co-tier and cross-tier backhaul interferences on downlink signal-to-interference ratio (SIR) coverage of small cell users is investigated. Numerical results demonstrate that implementing only either the in-band or out-of-band backhauling solutions may not be useful. Instead, a hybrid system with correct proportion of in-band and out-of-band small cells should be implemented.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".