Toward Optimal Admission Control and Resource Allocation for LTE-A Femtocell Uplink
Bibliographic record
Abstract
The Third-Generation Partnership Project (3GPP) has incorporated femtocell (FC) technology in the Long-Term Evolution Advanced (LTE-A) standard to enhance the quality of service of indoor mobile users and extend the coverage area of existing macrocells (MCs). In such two-tier LTE-A MC/FC systems, cotier and cross-tier interference exists in cochannel deployment, exerting adverse effects on system performance. In this paper, we study the single-carrier frequency-division multiple-access (SC-FDMA)-based LTE-A FC uplink. We propose the use of transport-layer data admission control (AC) in femto user equipment (FUE) and interference-aware resource allocation (RA) in each base station (BS) to manage the intercell interference (ICI). We first formulate the problem as a constrained Markov decision problem (CMDP) that aims at maximizing the time-average throughput of the entire FC tier subject to the queue stability constraint for each FUE. Then, we propose a joint AC and RA (JACRA) algorithm to obtain the optimal AC and RA policies. In light of the NP-hardness of the RA subproblem, we further propose an iterative heuristic with polynomial time complexity. Simulation studies show that the proposed JACRA algorithm is throughput optimal, outperforming alternative proportional fair (PF) and round robin (RR) scheduling schemes. Moreover, the proposed heuristic achieves near-optimal throughput with substantial improvement in computational complexity.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".