A Novel Interference Alignment Scheme Based on Sequential Antenna Switching in Wireless Networks
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Bibliographic record
Abstract
Interference alignment (IA) is a promising technique that can effectively eliminate the interference in wireless networks. However, in traditional IA schemes, the signal to interference plus noise ratio (SINR) may significantly degrade, and the quality of service (QoS) may be unacceptable. In this paper, a novel IA scheme based on antenna switching (AS-IA) is proposed to improve the SINR of the received signal while guaranteeing the QoS in IA wireless networks. In the proposed scheme, some of the antennas are replaced by reconfigurable ones that can switch among preset modes, and the best channel coefficients are selected. Furthermore, to reduce the computational complexity, a sequential antenna switching IA (SAS-IA) scheme is proposed with only one antenna switching in each time slot, and the communication proceeds during the process of searching for the optimal solution. To further improve the performance of the SAS-IA scheme under imperfect channel state information (CSI), a filtering SAS-IA scheme is proposed through averaging the estimated CSI during the iterations of the distributed IA algorithm. Simulation results are presented to show the effectiveness and efficiency of the proposed schemes in improving the QoS of IA wireless networks.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it