Non-Centralized Quantum Neural Networks for Cell-Free MIMO Systems
Bibliographic record
Abstract
This paper propose a two-stage quantum neural network (QNN) framework for cell-free multiple-input and multiple-output (MIMO) wireless communication systems. Cell-free MIMO, which has been regarded as a key technology for enhancing the performance of the next-generation wireless communication systems, leverages the collective capability of multiple distributed access points (APs), allowing collaboration between them. However, optimizing cell-free MIMO can pose challenges for centralized optimization schemes. In particular, complexities associated with the joint optimizations of user-transmission assignment and transmission precoding, two factors which are of much importance for determining the quality-of-service, grow with the number of APs and served users. To this end, a unified scheme employing distributed QNNs is used to optimize downlink transmitter-user assignment and transmit precoding with the goal of maximizing the achieved sum rate. Firstly, the cloud processing unit, which holds holistic information about the particular wireless communication network, employs QNN to assign each AP to its designated mobile terminal. Secondly, the edge processing units, which are computed in proximity relative to the AP in order to reduce latency, estimate transmission precoding for their corresponding APs. Moreover, numerical results are presented to showcase the performance of the proposed protocol.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".