Multiple access interference suppression for CDMA systems via <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si3.svg"><mml:msub><mml:mi>ℓ</mml:mi><mml:mi>∞</mml:mi></mml:msub></mml:math>-minimization
Bibliographic record
Abstract
A key problem in code-division multiple access (CDMA) system is to mitigate the multiple access interference (MAI) from other users while detecting the desired user. The performance of the conventional minimum output energy (MOE) multiuser detector for CDMA system significantly degrades in the presence of signature waveform distortions induced by multipath propagation or timing asynchronism. In this paper, a robust linear programming (ROLP) algorithm for blind multiuser detection is proposed. Different from the existing MOE-based multiuser detection techniques, the proposed ROLP minimizes the ℓ∞-norm of the output to exploit the non-Gaussianity of the communication signals. To achieve robustness against signature waveform mismatch, the proposed method constrains the magnitude response of any signature vector within a specified uncertainty set to exceed unity. The uncertainty set is modeled as a rhombus, which differs from the spherical uncertainty region widely taken in the existing robust multiuser detectors. The resulting optimization problem is reformulated into a linear programming program and hence can be solved efficiently. The proposed ROLP is computationally simpler than its robust counterparts that requires solving a second-order cone programming. Simulation results demonstrate the superiority of the ROLP over several robust detectors, which indicate that its performance approaches the optimal performance bound.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".