Dynamic Grover Search Optimization with Deep Q-Networks for Active User Detection
Bibliographic record
Abstract
Sixth-generation (6G) networks must deliver ultra-low latency and near-100 percent reliability to support massive-scale Internet of Things (IoT) deployments and Hyper-Reliable Low-Latency Communications (HRLLC). Grant-free access protocols permit devices to transmit without prior scheduling; nevertheless, this uncoordinated transmission introduces uncertainty at the receiver, necessitating Active User Detection (AUD) to ascertain which devices are active. Quantum search methods—most notably Grover’s algorithm—can accelerate AUD, yet they require knowing the optimal number of iterations, which depends on the (typically unknown and time-varying) number of valid solutions induced by the current activity pattern and channel/noise conditions. To overcome this, we formulate an optimization problem that optimizes the number of Grover iterations to maximize detection accuracy and minimize computational cost without any prior activity information. We then apply a Deep Q-Network (DQN) to learn, via deep reinforcement learning, an adaptive policy for selecting the iteration count. Simulation results verify that the DQN converges to an optimal strategy and outperforms two baseline schemes under varying fading conditions and active-user transmit powers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".