Robust MSE-based transceiver optimization for downlink cellular interference alignment
Bibliographic record
Abstract
In this paper, we consider mean squared error (MSE)-based robust transceiver optimization for multi-user multi-input multi-out (MU-MIMO) cellular network that exploits interference alignment (IA) for its downlink communication. First, we consider the conventional sum-MSE minimization problem, where the MSE is calculated at user equipments (UEs). Second, we consider a different approach where the MSE can be calculated at base stations (BSs) i.e., leakage-based MSE from each BS. Different from conventional per-base station power constraint (PBPC), we assume practical per-antenna power constraint (PAPC) due to linearity of the power amplifier that feeds each transmit antenna at the BSs. To this end, we derive robust precoders and receive filters for these two design objectives under statistical channel state information (CSI) uncertainty. Simulation results suggest that our robust designs offer a good performance, showing resilience against CSI uncertainty. In addition, the sum-rate performances for designs with PAPC and PBPC are compared. It is observed that the sum-rate loss due to PAPC is relatively small for the robust designs.
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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".