Cross-tier interference mitigation in Femto-macro cellular architecture in downlink
Bibliographic record
Abstract
Deployment of femtocells faces a significant challenge emanating from the interference from the overlay of macros to the underlay of femtos. This paper focuses on the mitigation of this cross-tier interference in the downlink by limited coordination between femtocells and macrocell. Contesting the natural strategy of orthogonalized transmission thereby eliminating the interference, we propose a spectrum splitting strategy which is based on coordinating this cross-tier interference in a way that the resultant interference is effectively exploited thereby improving the system performance. The problem is posed as a single-agent control problem. Macro base station (BS) is an agent that dynamically swaps its macro users/constellations on its bandwidth to manage the interfering constellations that it produces to its femtocell users. The agent learns the optimal interfering constellation strategy using information from its femto BSs using reinforcement-learning with a Q-learning implementation. Simulation results illustrate the sum rate gain brought about by the proposed strategy of managing interfering constellations for their subsequent exploitation.
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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) | 0.000 | 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 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".