On the achievable rates in decentralized networks with Randomized Masking
Bibliographic record
Abstract
We address a two-user decentralized interference channel with static non-frequency selective channel gains. Both users are unaware of each other's code-books and there is no central controller to manage the allocation of resources between the two users. As multiuser detection is not possible, the conventional scheme of transmitting a continuous stream of i.i.d. symbols from Gaussian codebooks by each transmitter (referred to as continuous transmission) results in excessive interference. To provide both users with a partially interference-free channel, we propose that each user randomly quits transmitting from transmission slot to transmission slot independently with a probability of 1 - ε; ε ∈ (0, 1). This is called the Randomized Masking (RM) protocol. Due to the on-off nature of transmissions, the noise plus interference process has a mixed distribution. As a result, the mutual information between the input and the output of the channels does not accept any closed form expression. Assuming each user transmits i.i.d. signals upon activation, the highest achievable rate by each user is denoted by C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RM-I</sub> . We derive upper and lower bounds on C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RM-I</sub> where Entropy Power Inequality (EPI) and the extremal inequality of Liu and Viswanath are two important tools in this analysis. Using the proposed lower bound, we devise a distributed strategy to select the activity factor e. Note that this strategy includes the conventional continuous transmission by setting ε = 1. The main result of the paper states that there exist values of 0 ≤ α <; 1/2 <; β ≤ 1 such that for all ε ∈ (α, β) it is possible to achieve rates larger than C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RM-I</sub> as far as the Signal-to-Noise Ratio (SNR) is sufficiently large. Therefore, it is proved that transmitting i.i.d. signals in consecutive transmission slots is not optimum under the RM protocol.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".