Opportunistic joint decoding with scheduling and power allocation in OFDMA femtocell networks
Bibliographic record
Abstract
One of the major challenges in deploying femtocells is the management of interference between neighboring femtocells and between the femtocells and the macrocells. This paper explores the use of opportunistic multiuser detection for interference mitigation in the downlink of an orthogonal frequency division multiple access (OFDMA) femtocell network. In particular, we focus on the use of joint decoding (JD) at the receiver, where a macro or femto user may jointly decode both the desired message and the message from a selected interfering user in order to achieve a higher overall transmission rate. It is shown that to take the full advantage of opportunistic multiuser detection, the selection of JD pairs needs to be jointly optimized with scheduling, power allocation, and rate adaptation. This paper adopts a network utility maximization framework and proposes an iterative algorithm for such a joint optimization across the network. Simulation results show that multiuser detection can significantly benefit the femto-users, while maintaining the performance of macro-users. Further, although the lower-complexity successive interference cancellation (SIC) scheme can already reap significant benefit of multiuser detection, JD can further improve upon SIC.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".