Distributed Multi‐IRS‐assisted 6G Wireless Networks: Channel Characterization and Performance Analysis
Bibliographic record
Abstract
In this chapter, we study the channel modelling and characterization for multi-intelligent reconfigurable surface (IRS)-assisted wireless systems. Specifically, we consider a general system model, called a distributed multi-IRS (DMI)-assisted system, in which the IRSs have different geometric sizes and are distributively deployed to aid wireless communications. For the purposes of comprehensive channel modelling, we assume that wireless channels, associated with different IRSs, are independent but not identically distributed (i.n.i.d.). To statistically characterize such a channel, we propose a mathematical framework based on the moment-matching method to determine the distribution of the end-to-end (e2e) channel fading of the DMI system. We prove that the true distribution of the e2e channel magnitude can be approximated by either Gamma or log-normal distributions. Using the obtained approximate distributions, we derive tight approximate closed-form expressions of the ergodic capacity (EC) and outage probability (OP) of the DMI system. Numerical results show that the DMI system outperforms the conventional non-IRS-assisted system in terms of EC and OP. Furthermore, considering i.n.i.d. fading channels, the co-channel interference and the number of reflecting elements installed on each IRS have a significant impact on the DMI system performance.
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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 | 0.001 | 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 | 0.001 |
| 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".