Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks
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Bibliographic record
Abstract
In this paper, we consider joint beamforming, power, and channel allocation in a multiuser and multichannel underlay multiple-input-single-output (MISO) cognitive radio network (CRN). In this system, the primary users' spectrum can be reused by secondary-user transmitters (SU-TXs) to maximize spectrum utilization, whereas intrauser interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a nonconvex mixed-integer nonlinear programming problem, we propose a solution that consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and a semidefinite relaxation approach. In the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that the beamforming and power and channel allocation with SA algorithm can achieve a close-to-optimal sum rate while having lower computational complexity compared with the beamforming and power and channel allocation with the GA algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum rate compared with the existing zero-forcing beamforming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it