Optimal Power/Rate Allocation and Code Selection for Iterative Joint Detection of Coded Random CDMA
Why this work is in the frame
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Bibliographic record
Abstract
Iterative interference cancellation of coded code-division multiple access (CDMA) using random spreading with linear cancellation is analyzed. If users are grouped into power classes and Shannon bound approaching codes are used, a geometric power distribution achieves the additive white Gaussian noise (AWGN) channel Shannon bound as the numbers of classes becomes large. The optimal distribution of the size of these classes is shown to be uniform. If users are grouped into different rate classes with equal powers among equal rate users, the Shannon bound for AWGN channels can be achieved with an arbitrary distribution of the classes sizes, provided that the size of the largest rate class obeys the mild condition that its ratio of size to processing gain is much smaller than the inverse of the signal-to-noise ratio (SNR). The case of equal powers and equal rates among all users is addressed as a "worst case" scenario. It is argued that simple repetition codes provide for a larger achievable capacity than stronger codes. It is shown that this capacity monotonically increases as the rate of the code decreases. A density evolution analysis is used to show that the achievable rates exceed those of a minimum-mean square error filter applied to the uncoded signals. This lower bound is tight for small ratios of bit energy to noise power, and otherwise the iterative cancellation receiver provides an appreciably larger capacity. Relating to recent result from the application of statistical mechanics it is shown that the repetition-coded system with iterative cancellation achieves the performance of an equivalent optimal joint detector for uncoded transmission
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it