Sparse beamforming for limited-backhaul network MIMO system via reweighted power minimization
Bibliographic record
Abstract
This paper considers a downlink multicell cooperation model in which the base-stations (BSs) are connected to a central processor (CP) via rate-limited backhaul links. A user-centric clustering model is adopted where each scheduled user is cooperatively served by a cluster of BSs, and the serving BSs for different users may overlap. This paper formulates an optimal joint clustering and beamforming design problem in which each user dynamically forms a sparse network-wide beamforming vector whose non-zero entries correspond to the serving BSs. Specifically, we assume a fixed signal-to-interference-and-noise ratio (SINR) constraint for each user, and investigate the optimal tradeoff between the sum transmit power and the sum backhaul capacity needed to form the cooperating clusters. Intuitively, larger cooperation size leads to lower transmit power, because interference can be mitigated through cooperation, but it also leads to higher sum backhaul, because user data needs to be made available to more BSs. Motivated by the compressive sensing literature, this paper formulates the sparse beamforming problem as an ℓ0-norm optimization problem, then uses the iterative reweighted ℓ1heuristic to find a solution. A key observation of this paper is that the reweighting can be done on the ℓ2-norm square of the beamformers (i.e., the power) at the BSs. This gives rise to a weighted power minimization problem over the entire network, which can be solved using the uplink-downlink duality technique with low computational complexity. This paper further proposes judicious choice of the weights, and shows that the new algorithm can provide a better tradeoff between the sum power and the sum backhaul capacity in the high SINR regime than previous algorithms.
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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 | 0.000 | 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".