Intelligent Reflecting Surfaces Assisted UAV Communications for IoT Networks: Performance Analysis
Bibliographic record
Abstract
The increasing demand for wireless connectivity and the emergence of the notion of the Internet of Everything require new communication paradigms that will ultimately enable a plethora of new applications and new disruptive technologies. In this context, the present contribution investigates the use of the recently introduced intelligent reflecting surface (IRS) concept in unmanned aerial vehicles (UAV) enabled communications aiming to extend the network coverage and improve the communication reliability as well as spectral efficiency of Internet of Things (IoT) networks. In particular, we first derive tractable analytic expressions for the achievable symbol error rate (SER), ergodic capacity, and outage probability of the considered set up. Following this, we also derive tight upper and lower bounds on the average signal-to-noise ratio (SNR). Our derivations are then compared with the corresponding asymptotic performance, based on the central limit theorem (CLT) assumption, which reveals that the asymptotic SNR falls within the area between derived bounds, and approaches either bound depending on the number of reflective elements (REs). We further show that the asymptotic SER becomes in a tight agreement with the corresponding exact simulation SER for N ≥ 16. In addition, the offered results demonstrate that the use of the IRS is significantly effective as they assist in improving the achievable SER by five orders of magnitude. We further demonstrate that, in terms of achievable ergodic capacity, IRS-assisted UAV communication systems can exhibit ten times higher capacity compared to conventional UAV communications. Based on the above, these results and related insights are anticipated to be useful in the design and deployment of IRS-assisted UAV systems in the context of beyond 5G communications, such as 6G communications.
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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 | 0.003 |
| 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".