Power Control in Spectrum Sharing Systems with Almost-Zero Inter-System\n Signaling Overhead
Bibliographic record
Abstract
Power allocation in spectrum sharing systems is challenging due to excessive\ninterference that the secondary system could impose on the primary system.\nTherefore, an interference threshold constraint is considered to regulate the\nsecondary system's activity. However, the primary receivers should measure the\ninterference and inform the secondary users accordingly. These cause design\ncomplexities, e.g., due to transceiver's hardware impairments, and impose a\nsubstantial signaling overhead. We set our main goal to mitigate these\nrequirements in order to make the spectrum sharing systems practically\nfeasible. To cope with the lack of a model we develop a coexisting deep\nreinforcement learning approach for continuous power allocation in both\nsystems. Importantly, via our solution, the two systems allocate power merely\nbased on geographical location of their users. Moreover, the inter-system\nsignaling requirement is reduced to exchanging only the number of primary users\nthat their QoS requirements are violated. We observe that compared to a\ncentralized agent that allocates power based on full (accurate) channel\ninformation, our solution is more robust and strictly guarantees QoS\nrequirements of the primary users. This implies that both systems can operate\nsimultaneously with almost-zero inter-system signaling overhead.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".