Generalized Superposition Modulation and Iterative Demodulation: A Capacity Investigation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Modulation with correlated signal waveforms is considered. Such correlation arises naturally in a number of modern communications systems and channels, for example, in code‐division multiple‐access (CDMA) and multiple‐antenna systems. Data entering the channel in parallel streams either naturally or via inverse multiplexing is transmitted redundantly by adding additional signal waveforms populating the same original time‐frequency space, thus not requiring additional bandwidth or power. The transmitted data is spread over a frame of N signaling intervals by random permutations. The receiver combines symbol likelihood values, calculates estimated signals and iteratively cancels mutual interference. For a random choice of the signal waveforms, it is shown that the capacity of the expanded waveform set is nondecreasing and achieves the capacity of the Gaussian multiple access channel as its upper limit when the number of waveforms becomes large. Furthermore, it is proven that the iterative demodulator proposed here can achieve a fraction of 0.995 or better of the channel capacity irrespective of the number of transmitted data streams. It is also shown that the complexity of this iterative demodulator grows only linearly with the number of data streams.
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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 | 0.000 | 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 | 0.000 |
| 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