Optimal Spectrum Leasing and Resource Sharing in Two-Way Relay Networks
Bibliographic record
Abstract
Assuming a bidirectional relay assisted network, we study the problem of optimal resource sharing between two transceiver pairs. One pair, referred to as the primary pair, is considered to be the owner of the spectral resources. The rates of the two transceivers in this pair must be guaranteed to be greater than a predefined threshold. The second pair, called the secondary pair, is assumed to own the relay infrastructure. The secondary network allows the primary pair to use the relays to establish a bidirectional communication between its transceivers. In exchange for this cooperation, the primary pair assigns a portion of its resources to the secondary pair, thereby enabling a two-way communication between the secondary transceivers. Assuming amplify-and-forward relaying scheme, the relays collectively build two network beamformers each of which enables communication between the two transceivers in one pair. Aiming to optimally calculate the parameters of the two networks, we study three different design problems. The first approach relies on maximizing the smaller of the secondary transceiver rates subject to two separate constraints on the total power levels consumed in the primary and the secondary networks while providing a minimum data rate to the primary pair. In the second approach, we consider a constraint on the average total power consumed in both networks, while maximizing the smaller of the secondary transceiver rates. The third approach extends the second method to materialize a spectrum leasing and sharing scheme for the case when the primary network is active with a certain probability.
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.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".