Reconfigurable Intelligent Surface as a Micro Base Station: A Novel Paradigm for Small Cell Networks
Bibliographic record
Abstract
Small cell networks (SCNs) have emerged as a promising solution to meet the demand for increasing data traffic for the sixth generation and beyond wireless networks. However, power consumption and two-tier interference issues are two bottlenecks that hinder further development. This paper proposes a novel reconfigurable intelligent surface (RIS)-based SCN in which an RIS serves multiple micro users as a small cell base station while assisting the macro user’s transmission. Compared to the conventional SCNs, the RIS-based SCN can achieve significant power reduction. Meanwhile, the reflected signal can be regarded as a multipath component instead of interference to the macro user. We propose two transmission schemes and formulate the design of the phase shift matrix at the RIS and the beamforming vector at the macro base station as an optimization problem. The alternating optimization algorithm is developed to optimize the phase shift matrix and the beamforming vector to minimize the total power consumption under the user rate and phase shift constraints. Simulation results show that the total power consumption can be reduced significantly by deploying the RIS in the SCN when the number of reflective elements is sufficiently large.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".