Interference Reduction by Beamforming in Cognitive Networks
Bibliographic record
Abstract
We consider beamforming in a cognitive network with multiple primary users and secondary users sharing the same spectrum. In particular, we assume that each secondary transmitter has Nt antennas and transmits data to its single- antenna receiver using beamforming. The beamformer is designed to maximize the cognitive user's signal-to-interference ratio (SIR), defined as the ratio of the received signal power at the desired cognitive receiver to the total interference created at all the primary receivers. Using mathematical tools from random matrix theory, we derive both lower and upper bounds on the average interference at the primary receivers and the average SIR of the cognitive user. We further analyze and prove the convergence of these two performance measures asymptotically as the number of antennas Ntor primary users Ntincreases. Specifically, the average interference per primary receiver converges to the expected value of the path loss in the network whereas the average SIR of the secondary user decays as 1/c when c = Np/Ntrarr infin. In the special case of Nt= Np, the average total interference approaches 0 and the average SIR approaches infin.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".