Iterative tree search detection for MIMO wireless systems
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".