Distributed beamforming in cognitive multi-cell wireless systems by fast interference coordination
Bibliographic record
Abstract
We consider the energy-efficient design of transmit beamforming in the downlink of a cognitive multi-cell wireless system. In this system, multiple secondary cells minimize their total transmit power while satisfying given SINR requirements for the secondary users. At the same time, the secondary base stations are not allowed to exceed aggregate interference temperature limits to the users of a primary system. We propose a distributed optimization framework to decompose this multi-cell problem into parallel single-cell beamforming subproblems. As a result, the cognitive cells independently compute their beamformers using only local channel information. Moreover, they coordinate their inter-cell interference as well as the interference temperature to the primary users by exchanging some signaling information through a backhaul. This coordination is achieved through a consensus-based optimization. In contrast to classical decomposition methods, our approach is based on the alternating direction method of multipliers. Therefore, it leverages the augmented Lagrangian technique to accelerate the interference coordination between the secondary cells, thanks to the quadratic penalty terms that are added in the objective function with the standard pricing terms. Numerical results are provided to verify the effectiveness of this distributed algorithm.
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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 | 0.001 |
| 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".