Evaluating the Impact of QAM Constellation Subset Selection on the Achievable Information Rates of Multidimensional Formats in Fully Loaded Systems
Bibliographic record
Abstract
An efficient procedure is presented for evaluating the performance of multidimensional modulation formats in terms of the achievable information rate (AIR). It allows the explicit properties of signal constellations to be captured and is applicable to fully loaded dense wavelength-division multiplexed transmission systems. The efficiency of the procedure facilitates formulating multidimensional quadrature amplitude modulation (QAM) constellation subset selection as a combinatorial optimization problem. The attained solutions for the quadrature phase shift keying (QPSK) 8D constellation subset selection suggest that a known 8D power and polarization balanced constellation (PPB constellation, 4 bits/8D symbol) and its different variations are the closest 8D QPSK subsets to the Shannon limit at 4 bits/8D symbol. 8D constellation subset selection of a QPSK constellation at 6 bits/8D symbol allows obtaining an 8D polarization balanced version of polarization-switched QPSK (PB-PS-QPSK). Using the proposed procedure, the performance of these constellations and their nonpolarization balanced counter parts, i.e., dual polarization binary phase shift keying (DP-BPSK) and PS-QPSK, is assessed in terms of the estimated AIR. The results exhibit good agreement with those of full system simulations for a single channel and five channels. Moreover, the impact of QAM constellation subset selection on the system performance is evaluated by comparing the reduction in information rate for a symbol rate of 35 Gbaud as a function of the number of channels. For 41 channels, the PPB constellation outperforms DP-BPSK by 2 Gb/s in information rate for a 12,000 km dispersion-managed (DM) link due to the improved linear and nonlinear constellation properties. Finally, PB-PS-QPSK enables an increase of 1.5 Gb/s in information rate compared to PS-QPSK for a 10,000 km DM link.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".