Receiver Design and Performance Evaluation for MIMO Integrated GSM Systems
Bibliographic record
Abstract
We investigate feasibility of multiple-input-multiple-output (MIMO) technology integration with the existing GSM transceiver architecture. System level performance gains are explored for the full-rate GSM data traffic channel combined with multiple-antenna uplink transmission with ntautransmit and nR receive antennas. We consider the typical urban environment with Rayleigh fading, when the channel state information is available at the receiver only. The designed receiver adopts a linearly complex MIMO signal detector based on the space matched filtering principle [8] for the GMSK modulation used in the GSM standard. The detector is further integrated with two iterative equalizers that use either iterative turbo equalization or probabilistic data association to provide additional performance gains. Simulated bit-error rate performance results for various antenna configurations illustrate that the MIMO integration can provide dramatic performance gains when compared to the traditional single-antenna GSM system. A potential application for this technology is high rate uploading of multimedia contents generated by mobile users.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".