Joint User Grouping and Transceiver Design in a MIMO Interfering Broadcast Channel
Bibliographic record
Abstract
Consider a MIMO multi-cellular network (also known as an interfering broadcast channel) where each base station transmits signals to the users in its own cell. The basic problem is to design linear transmit/receive beamformers and schedule users across a fixed set of time slots so as to maximize the system throughput in the presence of both inter and intra cell interference. In this paper, we propose a joint linear transceiver design and user grouping scheme for sum utility maximization that is based on iterative minimization of weighted mean squared error (MSE). The proposed algorithm only needs local channel knowledge and its convergence to a stationary point is guaranteed for some well-known utility functions, while ensuring user fairness. The simulation results show that the proposed formulation/algorithm can offer significantly higher system throughput than the standard multi-user MIMO techniques such as the SVD-MMSE strategy, while maintaining user fairness. Furthermore, the proposed algorithm exhibits fast convergence and is amenable to distributed implementation with limited information exchange.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".