Efficient Scheduling Algorithms for Multiantenna CDMA Systems
Bibliographic record
Abstract
In multiple-input-multiple-output (MIMO) multiuser systems, simultaneously serving multiple users achieves high data rates. However, high-performance transmit beamforming requires an adequately designed user-selection scheme. Optimal scheduling can be only obtained through a high computationally complex exhaustive search, and hence, low-complexity heuristic algorithms are required. In addition, employing a multiple-access scheme such as code division (CDMA) largely increases the complexity of optimal scheduling, and it becomes unemployable even for a moderate number of users and antennas. In this context, this paper proposes three heuristic scheduling algorithms for MIMO CDMA systems using zero-forcing beamforming (ZFBF). We use a graph-theoretical approach to model the system as a weighted undirected graph. The problem of user selection is then formulated as a graph coloring problem, namely, the maximum weightN-colorable subgraph problem. Then, we design two heuristics to solve this graph problem. The first algorithm is a low-complexity greedy algorithm. The second algorithm is based on a tabu search approach to resolve efficiently the complexity/performance tradeoff. Numerical and simulation results show the sub-optimal performances and robustness of the proposed low-complexity 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".