Joint Network Coding and Subcarrier Assignment in OFDIMA-Based WVireless Networks
Bibliographic record
Abstract
Orthogonal frequency division multiple access (OFDMA) has been integrated into emerging broadband wireless access technologies such as 802.16 wirelessMAN. Due to the diversity of channel gains among the downlink subscribers, it is known that dynamic allocation of subcarriers can significantly improve the overall performance of OFDMA systems, in terms of power efficiency and link throughput. A large body of work has focused on the joint subcarrier assignment and resource (bit and power) allocation for the OFDMA downlink. In this paper, we adopt a cross layer approach towards a network coding aware subcarrier assignment algorithm for the uplink and downlink of OFDMA based wireless networks. We formulate the maximal rate assignment problem as a mixed integer linear program and derive a polynomial time heuristic to approximate the solution. With network coding, it becomes possible to assign the same subcarrier to different downlinks without causing any interference. Consequently, our coding-aware assignment scheme improves the bandwidth efficiency and increases the network layer throughput by a substantial margin. We show that the total network throughput resulting from the heuristic is comparable to the optimal solution, with slight compromise of fairness. In addition, the coding aware subcarrier assignment mechanism can be applied to other multichannel wireless systems as well.
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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 | 0.000 | 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 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".