Efficient Heuristic for Resource Allocation in Zero-Forcing OFDMA-SDMA Systems with Minimum Rate Constraints
Bibliographic record
Abstract
Multi-antenna OFDMA-SDMA systems provide the required high spectral efficiency and flexibility to support the ever increasing data rates requirements of real-time multimedia applications in future wireless access systems. However, the resource allocation process becomes extremely complex because of the large number of degrees of freedom and the strict timing requirement of real-time traffic. In this paper, we propose heuristics to efficiently solve the zero-forcing OFDMA-SDMA resource allocation problem and provide, when feasible, guaranteed service to users with minimum rate requirements. The heuristics combine both rate-constrained power allocation and subcarrier reassignment algorithms. We compare the heuristics performance against an upper bound and other methods proposed in the literature and find that, although they have a slightly lower sum rate performance, they support a wider range of minimum rates while significantly reducing the computational complexity, making them suitable for usage in real-time systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".