Efficient User Selection and Ordering Algorithms for Successive Zero-Forcing Precoding for Multiuser MIMO Downlink
Bibliographic record
Abstract
In this paper we consider user scheduling problem for linearly preceded multiuser multiple-input multiple-output (MIMO) downlink, where base station as well as the mobile receivers are equipped with multiple antennas. Optimal precoding involves dirty paper coding (DPC) technique, and it is highly nonlinear and complex. On the other hand, complete inter-user interference cancellation using linear zero-forcing or block diagonalization precoding are suboptimal. Hence, we consider successive zero-forcing precoding, which achieves improved system throughput compared to block diagonalization by allowing users to work under limited interference. Due to the dimensionality constraint of linear precoding techniques user scheduling is required. The optimal user scheduling involves exhaustive search, which becomes very complex for realistic numbers of users and transmit antennas. In addition, for successive zero-forcing precoding the order in which users are precoded successively is important for sum rate maximization, which further increases the complexity of exhaustive search. In this paper we develop a low complexity greedy user scheduling algorithm for successive zero-forcing precoding, which incorporates various user ordering techniques. Simplified heuristic scheduling metrics are proposed, which are shown to perform close to the exhaustive search method. A suboptimal user ordering technique that is similar to the order, in which the proposed greedy user selection selects users, is proposed. Further simplification of regular greedy scheduling algorithm is obtained with the proposed intermediate user grouping technique. The proposed algorithm is of low complexity, but provides performance close to the highly complex exhaustive search algorithm.
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 | 0.000 |
| 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".