Uplink resource allocation for interworking of WLAN and OFDMA-based femtocell systems
Bibliographic record
Abstract
Efficiency of the wireless local area network (WLAN)/femtocell interworking system essentially relies on the efficiency of the resource allocation protocol employed in the system. Efficiency of the resource allocation protocol depends on whether it has been designed considering physical layer and medium access control layer technologies of different networks in the interworking system. Therefore, in this paper, we formulate a resource (user, subcarrier, and power) allocation problem for maximizing the sum of weighted rates of the interworking system considering multi-homing capable users and the main features of IEEE 802.11 distributed coordination function (DCF) and orthogonal frequency division multiple access (OFDMA) based femtocell networks. Solving this problem optimally is prohibitively complex as it is a non-convex problem. Thus, the problem is sub-optimally solved by dividing it to two sub-problems. A heuristic algorithm is proposed for user and subcarrier allocation while an optimal and fast converging power allocation algorithm is derived based on dual decomposition and the characteristics of Lagrangian. Simulation results have shown that the proposed resource allocation protocol achieves results close to the optimum.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".