Iterative tree search detection for MIMO wireless systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a reduced-complexity detection scheme, called iterative tree search (ITS) detection, with application in iterative receivers for multiple-input multiple-output (MIMO) wireless communication systems. In contrast to the optimum maximum a posteriori (MAP) detector, which performs an exhaustive search over the complete set of possible transmitted symbol vectors, the aim of the new scheme is to evaluate only the symbol vectors that contribute significantly to the soft output of the detector. To this end, a list of "good" candidate symbol vectors is generated prior to the actual computation of the detector output, with the aid of a sequential tree searching scheme based on the M-algorithm. For high-order QAM modulation formats, the complexity of the ITS detector can be further reduced with the aid of a special type of bit mapping called multi-level mapping. This results in a complexity per bit that is linear in the number of transmit antennas and roughly independent of the modulation order. Results from computer simulations are presented which demonstrate the good performance of the new scheme over a quasi-static Rayleigh fading channel, even for relatively small list sizes.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it