On the Performance of Cooperative Relaying Spectrum-Sharing Systems with Collaborative Distributed Beamforming
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Bibliographic record
Abstract
In this paper, we use joint distributed beamforming and cooperative relaying in cognitive radio relay networks in an effort to enhance the spectrum efficiency and improve the performance of the cognitive (secondary) system. In particular, we consider a spectrum sharing system where a set of potential relays are employed to help a pair of secondary users in the presence of a licensed (primary) user. Among the available relays, only the reliable ones participate in the beamforming process, where the beamformer weights are obtained based on a linear optimization method. We investigate two well-known strategies, namely, selection decode-and-forward (SDF) and amplify-and-forward (AF) relaying in conjunction with distributed optimal beamforming. However, given the complexity of the performance analysis with optimal beamforming, we use zero forcing beamforming (ZFB), and compare both approaches through simulations. In this context, for SDF, we derive expressions for the probability density function (PDF) of the received signal-to-interference noise ratio (SINR) at the relays as well as at the secondary destination. As for the AF scheme, we obtain the exact expression for the cumulative distribution function (CDF) and the moment generating function (MGF) of the equivalent end-to-end SNR at the secondary destination. For both schemes, we derive closed-form expressions for the outage probability and bit error rate (BER) over independent and identically distributed Rayleigh fading channels for binary phase shift keying (BPSK) and M-ary quadrature amplitude modulation (M-QAM) schemes. Numerical results demonstrate the efficacy of the proposed scheme in improving the outage and BER performance of the secondary system while limiting the interference to the primary system. In addition, the results show the effectiveness of the combination of the cooperative diversity and distributed beamforming in compensating for the loss in the secondary system's performance due to the primary user's co-channel interference (CCI).
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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 it