An eigen-assisted noncoherent receiver for Alamouti-type space-time modulation
Bibliographic record
Abstract
We consider noncoherent block detection of Alamouti-type space-time (ST) modulations, employing PSK constellations in quasi-static Rayleigh-fading channels with L receive antennas. The proposed detector, termed an eigen-assisted (EA) receiver, constructs a sample-correlation matrix from the L length-N received signals, determines its two principal eigenvectors, and uses these eigenvectors to reconstruct the two transmitted length-N patterns. Scalar differential encoding is performed at the transmit antennas, and as a result, the transmitted data can be recovered from the reconstructed patterns using scalar multiple-symbol differential detection. In other words, ST-differential encoding is not required at the transmitter and the constellation expansion typically observed with nonbinary signaling is avoided; a highly desirable result under a peak power constraint. Furthermore, the performance of the proposed EA-receiver is only 0.25 dB away from the coherent detection (with differential encoding) lower bound for the modulations considered. For quadrature phase-shift keying at a bit-error rate of 10/sup -4/, our EA-receiver with N=64 outperforms a decision feedback detector by 1 dB (L=1) and conventional ST-differential detection by more than 2.5 dB (L=2). Note that the complexity of our receiver, per symbol decision, is essentially independent of N and is comparable to that of a conventional ST-differential detector. The conclusion is reached that the proposed encoder/receiver pair is a promising alternative to currently known noncoherent techniques employing Alamouti-type ST-modulations.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".