On Mutual Interference Analysis in Hybrid Interweave-Underlay Cognitive Communications
Bibliographic record
Abstract
In this paper, we study the mutual interference in the context of a hybrid interweave-underlay cognitive communication where transmission constraints from both primary and secondary networks are considered. The transmission of a secondary user in such a network is generally constrained by the primary network so as to avoid harmful interference to the primary receiver. In addition, we consider a constraint from the secondary network to avoid harmful interference to other secondary receivers. To this end, the transmission probability of a secondary user is derived from the joint density function of the distances from secondary users to the primary receiver and that of between secondary users. Inspired by inherent benefits of an interweave-underlay hybrid approach, we further consider the impact of spectrum sensing on the transmission probability of the secondary users. The derived expression allows us to analyze the interdependency of protection margins that may be enforced by the primary and secondary networks. Relying on this analysis, we then derive closed-form expressions for expected aggregated interference to the primary and secondary receivers. Furthermore, we propose a power control approach enabled by an in-band signaling link to minimize the mutual interference to the receivers in the network. In order to show the effectiveness of the proposed interference modeling, we simulate a cognitive network with such constraints from both networks and evaluate the aggregated interference experienced by the receivers. Finally, we validate our interference model by comparing theoretical and simulated aggregated interference experienced by the receivers.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".