Investigation of multiuser detection schemes for Multiple Transmit/Multiple Receive antenna aided OFDM-SDMA systems
Bibliographic record
Abstract
Multiple Transmit-Multiple Receive (MTMR) Orthogonal Frequency-Division Multiplexing /Space-Division Multiple Access (OFDM-SDMA) systems have recently attracted significant interests among researchers and developers because of its potentials for realizing spectrally-efficient wireless services at low complexity and costs. However, in multiuser MTMR systems, multiuser detection (MUD) techniques become more challenging, owing to the increased number of independent transmitter-receiver links, especially with multiple antennas at both ends. This paper investigates the performance of adaptive beamforming schemes suitable for multiuser MTMR OFDM-SDMA systems where both mobile users and base stations employ multiple transmit antennas. Using performance criteria such as mean-squared error (MSE) and signal-to-interference-plus noise ratio (SINR), we evaluate the performance of MTMR maximum ratio combining (MTMR-MRC) and the MTMR minimum mean-squared error (MTMR-MMSE) systems. In both schemes, it is shown that the level of interference dominates the detection of users' signal causing high error floor at low SINR and imposing restrictions on the number of transmit antennas.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".