Efficient Resource Allocation with Flexible Channel Cooperation in OFDMA Cognitive Radio Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, a cooperative paradigm for single-channel cognitive radio networks has been advocated, where primary users can leverage secondary users to relay their traffic. However, it is not clear how such cooperation can be exploited in multi-channel networks effectively. Conventional cooperation entails that data on one channel has to be relayed on exactly the same channel, which is inefficient in multi-channel networks with channel and user diversity. Moreover, the selfishness of users complicates the critical resource allocation problem, as both parties target at maximizing their own utility. This work represents the first attempt to address these challenges. We propose FLEC, a novel design of flexible channel cooperation. It allows secondary users to freely optimize the use of channels for transmitting primary data along with their own data, in order to maximize performance. Further, we formulate a unifying optimization framework based on Nash Bargaining Solutions to fairly and efficiently address resource allocation between primary and secondary networks, in both decentralized and centralized settings. We present an optimal distributed algorithm and sub-optimal centralized heuristics, and verify their effectiveness via realistic simulations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it