Cooperative Beamforming for Cognitive-Radio-Based Broadcasting Systems in Presence of Asynchronous Interference
Bibliographic record
Abstract
To address the asynchronous interference issue in a generalized scenario with multiple primary and multiple secondary receivers, in this paper, we propose an innovative cooperative beamforming technique. The cooperative beamforming design is formulated as an optimization problem that maximizes the weighted sum transmission rate of secondary destinations while maintaining the asynchronous interference at the primary receivers below their target thresholds. In particular, we obtain the optimum beamforming directions and allocate power values among such beamforming directions. In light of the intractability of the power allocation problem, an approximation is used to convert the nonconvex and nonlinear optimization problem into a convex and linear one. Due to the multiple interference constraints corresponding to multiple primary receivers, the power allocation scheme is still complex. Therefore, we also propose a low-complexity power allocation algorithm. The proposed beamforming technique is extended for the case when cooperating CR nodes (CCRNs) have statistical channel knowledge of the primary receivers by developing an upper bound on the probability of introducing asynchronous interference at a given primary receiver beyond a given threshold value. We also propose two CCRN selection strategies for cooperative beamforming.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".