SINR-Based Transceiver Design in the K-User MIMO Interference Channel Using Multi-Objective Optimization
Bibliographic record
Abstract
The K-user interference channel describes K wireless pairs sharing the same spectrum simultaneously through the use of multiple antennas. In this paper, we present fast joint beamformer design to simultaneously minimize leakage interference (LI) and maximize both the individual signal powers (SPs) and the SINRs. This is a multi-objective, multi- variable problem which is treated here for the first time. We show that a fixed point of a nonexpansive vector field is guaranteed. This fixed point is a solution because it is a stationary point of the equally weighted sum of objective functions. Moreover, this stationary point ensures a minimum quality of service (QoS) for all users. Simulation demonstrates that the solution of the LI-SP-SINR problem is superior to max min SINR by having both a lower symbol error rate and a higher sum rate. Finding the solution also has lower complexity than MSE minimization and the max min SINR problem.
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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".