Spatial stream scheduling in uplink multiuser MIMO systems with zero-forcing post-processing
Bibliographic record
Abstract
This paper considers an interference-aware user selection and resource allocation for uplink multiuser multiple-input multiple-output (MU-MIMO) systems. At the base station (BS), zero-forcing (ZF) with singular value decomposition (SVD) decoupling of spatial streams is deployed. First, two algorithms are developed for mobile stations (MS)s and their corresponding antennas assignment as well as power allocation. The objective is to maximize the total system sum rate by considering the impact of multiple access noise enhancement on the spatial stream capacity. Then, an additional scheme is proposed to maximize the weighted sum capacity of all admitted users, where the weights are chosen based on the state of users' buffers. The proposed resource allocation and scheduling algorithms operate in a reduced search space for the sub-optimum configurations targeting lower overall complexity but still offering close to optimum results in a statistical sense. Simulation results are provided to demonstrate the potential of the developed technique in terms of total system sum rate performance and the buffer behavior.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".