Sum-Rate Maximization in the Multicell MIMO Broadcast Channel With Interference Coordination
Bibliographic record
Abstract
This paper studies the precoding designs to maximize the weighted sum-rate (WSR) in a multicell multiple-input multiple-output (MIMO) broadcast channel (BC). We consider a multicell network under universal frequency reuse with multiple mobile stations (MS) per cell. With interference coordination (IC) between the multiple cells, the base-station (BS) at each cell only transmits information signals to the MSs within its cell using the dirty paper coding (DPC) technique, while coordinating the inter-cell interference (ICI) induced to other cells. The main focus of this work is to jointly optimize the encoding covariance matrices across the BSs in order to maximize the network-wide WSR. Since this optimization problem is shown to be nonconvex, obtaining its globally optimal solution is highly complicated. To address this problem, we consider two low-complexity solution approaches with distributed implementation to obtain at least locally optimal solutions. In the first approach, by applying a successive convex approximation technique, the original nonconvex problem is decomposed into a sequence of simpler problems, which can be solved optimally and separately at each BS. In the second approach, the WSR problem is solved via an equivalent problem of weighted sum mean squared error minimization. Both solution approaches will unfold the control signaling among the coordinated BSs to allow their distributed implementation. Simulation results confirm the convergence of the proposed algorithms, as well as their superior performances over schemes with linear precoding or no interference coordination among the BSs.
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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 | 0.000 |
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 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".