Simplified Antenna Selection and User Scheduling for Orthogonal Space-Division Multiplexing
Bibliographic record
Abstract
In this paper, we study simplified algorithms for user scheduling in conjunction with receive antenna selection (RAS) for two downlink multiuser orthogonal space-division multiplexing techniques: block diagonalization (BD) and successive optimization (SO). Proposed algorithms select user and antenna subsets that maximize total power gain of the equivalent channel through mutual null-space projections of the active user channels. With this approach, algorithms add the best user at a time from the set of users not selected yet to the set of selected users until the desired number of users have been selected. To reduce the complexity further, users whose channel correlation to the signal subspace of the previously selected users is under some specified threshold are grouped at each step of the algorithm such that next user is selected from that group. This avoids the search through all K - i remaining users at the ith step of the algorithm. Two RAS algorithms are proposed, which further enhance the power gain of the equivalent channel by selecting a subset that contributes most towards the sum rate. For SO, user selection accounts for the interference of the previously selected users and maximizes the ratio of the total power of the equivalent channel to the approximate interference power on the desired signal subspace. Proposed algorithms perform close to the previously proposed algorithms with much less complexity.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".