Impact of Spatial Correlation on Link Selection and Delay Performance in Multi-RIS Networks
Bibliographic record
Abstract
Reconfigurable Intelligent Surface (RIS) technology has emerged as a key enabler for enhancing the performance of wireless communication networks. We investigate the deployment of multiple RISs within a cellular coverage area to enable diverse link options. We analytically characterize the impact of spatial correlation among RIS elements and derive the signal-to-noise ratio (SNR) distribution for the direct link to the base station (BS) and the cascaded RIS-assisted link, under Nakagami-m fading. Building on this analysis, a comprehensive framework is developed to evaluate the average packet delay, addressing the gap in delay analysis under spatial correlation for multi-RIS systems. Moreover, we derive a closed-form condition to identify when the direct link outperforms RIS-assisted transmission, thus guiding link selection and RIS-user association based on delay performance. Numerical results highlight the substantial effect of spatial correlation on delay analysis and demonstrate that neglecting this factor can severely underestimate network performance by leading to suboptimal link selections. A high degree of correlation concentrates channel energy into a dominant eigenmode for single-user transmission, thereby enhancing the effective SNR. Furthermore, the results show that the proposed delay-based selection approach accurately evaluates feasible links with the aim of minimizing the average packet delay, achieving superior performance compared to benchmark methods.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".