Downlink MIMO multiuser detection with interference subspace rejection
Bibliographic record
Abstract
We proposed recently a new technique for multiuser detection in CDMA networks, denoted interference subspace rejection (ISR), and evaluated its performance on the uplink. This paper extends its application to the downlink (DL). On the DL the information about interference is sparse, e.g., spreading factor (SF) and modulation of interferers may not be known, which makes the task much more challenging. We present three new ISR variants, which require no prior knowledge of the interfering users. The new solutions are applicable to MIMO systems and can accommodate any modulation, coding, spreading factor, and connection type. A new dynamic power-assisted channelization code allocation (DACCA) technique significantly reduces implementation complexity at the receiving mobile. Simulations under practically reasonable conditions suggest that increased user capacities and data-rates are attainable with downlink interference subspace rejection (DLISR) and system capacity increases linearly with the number of antennas. Capacity gains are at least 3 dB over the single-user detector and increase to 8 dB for high data-rates with 16-QAM.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".