Robust transceiver optimization for underlay device-to-device communications
Bibliographic record
Abstract
In this paper, we study robust transceiver optimization for device-to-device (D2D) communications that aims for signal-to-interference-plus-noise-ratio (SINR) fairness among D2D users. We assume that the multi-antenna D2D users in the cellular network employ interference alignment (IA) for their communication. In addition to an interference power constraint that is set by the primary network (i.e., macrocell) in the underlay cognitive transmission, we also consider channel state information (CSI) imperfectness to obtain a robust transceiver design for the D2D users. Due to the non-convexity of the design problem, we resort to an alternating minimization technique. Numerical simulations demonstrate the performance of the proposed transceiver compared to the benchmark case of an IA system without primary network/macrocell (non-cognitive). It is observed that at low SNR and high CSI error with relaxed interference power constraint, the worst-case stream data rate of the D2D users are close to that of the users in non-cognitive IA system but performance degrades significantly with stringent interference power constraint.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".