Modeling and Analysis of Cooperative Relaying in Spectrum-Sharing Cellular Systems
Bibliographic record
Abstract
In this paper, spectrum-sharing technology is integrated into cellular systems to improve spectrum efficiency. Macrocell users are primary users (PUs), whereas those within local cells, e.g., femtocell users, or desiring cost-effective services, e.g., roamers, are identified as secondary users (SUs). The SUs share the spectrum resources of the PUs in an underlay way; thus, the transmit power of a secondary is strictly limited by the primary's tolerable interference power. Given such constraints, a cooperative relaying transmission between an SU and the macrocell base station (BS) is necessary. To guarantee the success of dual-hop relaying and avoid multihop relaying, a new cooperative paradigm is proposed, where an idle PU (instead of a secondary, as assumed in general) in the vicinity of a target SU is chosen to serve as a relaying node, due to the fact that any PU can always transmit to the macrocell BS directly. Moreover, a two-way relaying strategy is applied at the chosen relaying node to further improve spectral efficiency. Our results demonstrate that the proposed system is particularly suitable for delay-tolerant wireless services with asymmetric downlink/uplink traffic, such as e-mail checking, web browsing, social networking, and data streaming, which are the most popular applications for SUs in spectrum-sharing cellular networks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".