Cross-Channel Similarity Analysis and Application Using a Multidimensional Structural Measure
Bibliographic record
Abstract
To address the stringent requirements of full coverage and ultra-high data rates in next-generation mobile communications, it is essential to leverage the coexistence of multiple radio frequency (RF) systems operating in well-separated frequency bands within precisely defined scenarios. In this context, an investigation of frequency-dependent and environment-dependent channel characteristics by exploring the spatial and temporal correlations of multipath channels across different frequency bands and different environments is imperative. This paper introduces a structural Channel Similarity Index Measure (CSIM) that holistically evaluates multiple multipath parameters between two channels, including amplitude, phase, delay, angle of arrival (AoA) and angle of departure (AoD). Based on extensive field measurement campaigns and ray tracing simulations conducted across both centimeter wave (cmWave) and millimeter wave (mmWave) bands in typical indoor and outdoor scenarios, the proposed CSIM is proven to effectively measure similarity from specific dimensions as well as the statistical distributions, and the similarities between channels across different frequencies and different environments are presented. Moreover, the feasibility of out-of-band information-assisted beam search, enabled by cross-band channel similarity, is also validated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".