Signal Reception for Space-Time Differentially Encoded Transmissions over FIR Rich Multipath Channels
Bibliographic record
Abstract
With sophisticated signal and information processing algorithms, air interfaces with space-time (ST) coding and multiple reception antennas substantially improve the reliability of wireless links. This paper proposes a new receiver algorithm for differential ST coded transmissions over the finite-impulse-response (FIR) rich multipath fading channels. The symbol detection introduced in this paper is a deterministic subspace-based approach in a multiple-input and multiple-output (MIMO) system framework. The receiver (i) operates in a blind fashion without estimating the channel or its inverse and (ii) is able to work with a small number of signal samples and hence can be applied in the quasistatic channels. The proposed scheme employs multiple antennas at both sides of the transceiver and exploits both the antenna diversity and the multiple constant modulus (MCM) characteristics of the signaling. The receiver is able to blindly mitigate the intersymbol interference (ISI) in a rich multipath propagation environment, and this has been verified through the extensive Monte Carlo simulations.
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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 | 0.001 |
| 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 | 0.000 |
| 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".