Sum-Rate Maximization in the Multicell MIMO Multiple-Access Channel with Interference Coordination
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Bibliographic record
Abstract
This paper is concerned with the maximization of the weighted sum-rate (WSR) in the multicell MIMO multiple access channel (MAC). We consider a multicell network operating on the same frequency channel with multiple mobile stations (MS) per cell. Assuming the interference coordination mode in the multicell network, each base-station (BS) only decodes the signals for the MSs within its cell, while the inter-cell transmissions are treated as noise. Nonetheless, the uplink precoders are jointly optimized at MSs through the coordination among the cells in order to maximize the network weighted sum-rate (WSR). Since this WSR maximization problem is shown to be nonconvex, obtaining its globally optimal solution is rather computationally complex. Thus, our focus in this work is on low-complexity algorithms to obtain at least locally optimal solutions. Specifically, we propose two iterative algorithms: one is based on successive convex approximation and the other is based on iterative minimization of weighted mean squared error. Both solution approaches shall then reveal the structure of the optimal uplink precoders. In addition, we also show that the proposed algorithms can be implemented in a distributed manner across the coordinated cells. Simulation results show a significant improvement in the network sum-rate by the proposed algorithms, compared to the case with no interference coordination.
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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 | 0.001 |
| Open science | 0.001 | 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 it