Suboptimal multiple-symbol differential detection of MPSK with diversity reception
Bibliographic record
Abstract
Simon and Alouini (2001) have derived the structure of the optimal N-length differential detector for differentially encoded M-ary phase-shift keying signals, transmitted over a slow fading diversity channel. Even though there is no explicit phase estimation and compensation being performed at the receiver, it was demonstrated that as N increases, the error performance of this multiple-symbol differential detector approaches that of maximal-ratio combining (with differential encoding). Unfortunately, efficient implementation of this optimal receiver is not possible, and it exhibits a computational complexity exponential in N. The computational complexity can be reduced dramatically to N log N if the receiver first performs coherent combining of the received multichannel baseband signals, followed by single-channel multiple-symbol differential detection (MSDD). A coherent combiner is introduced that is capable of cophasing the multichannel signals without violating the fundamental nature of differential detection, i.e. to avoid explicit phase estimation. The proposed suboptimal coherent combining MSDD (CC-MSDD) receiver essentially consists of two differential detectors: one operating across diversity branches, and one operating across time. Owing to the dramatic reduction in complexity, a very large observation window is used with the CC-MSDD. With window size N=64, the asymptotic performance of the CC-MSDD is only 0.5 dB away from maximal-ratio combining with differential encoding. This translates into a 1.5 dB gain in power efficiency over conventional equal gain combining for a second-order diversity system. The authors also investigate suboptimal receivers which perform selection combining, followed by single-channel MSDD. Although these selection combining MSDD receivers provide less than satisfactory performance in stand-alone mode, they are promising candidates for the initial detector in blind, iterative coherent detectors that operate on the principle of decision feedback.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".