Sparse beamforming design for network MIMO system with per-base-station backhaul constraints
Bibliographic record
Abstract
This paper considers the joint beamforming and clustering design problem in a downlink network multiple-input multiple-output (MIMO) setup, where the base-stations (BSs) are connected to a central processor with rate-limited backhaul links. We formulate the problem as that of devising a sparse beamforming vector across the BSs for each user, where the nonzero beamforming entries correspond to that user's serving BSs. Differing from the previous works, this paper explicitly formulates the per-BS backhaul constraints in the network utility maximization framework. In contrast to the traditional utility maximization problem with transmit power constraint only, the additional backhaul constraints result in a discrete ℓ0-norm formulation, which makes the problem more challenging. Motivated by the compressive sensing literature, we propose to iteratively approximate the per-BS backhaul constraints using a reweighted ℓ1-norm technique and reformulate the backhaul constraints as weighted per-BS power constraints. This allows us to solve the weighted sum rate maximization problem through a generalized weighted minimum mean square error (WMMSE) approach. To reduce the computational complexity of the proposed algorithm within each iteration, we propose two additional techniques, iterative link removal and iterative user pool shrinking, which dynamically decrease the potential BS cluster size and user scheduling pool. Numerical results show that the proposed algorithm can significantly improve the system throughput as compared to the naive BS clustering strategy based on the channel strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".