Intelligent Reflecting Surfaces Assisted Cellular V2X Based Open RAN Communications
Bibliographic record
Abstract
Open radio access networks (RAN) enhance the capabilities of traditional RAN by introducing features such as interoperability, open interfaces, software/hardware separation, and intelligence. Open RAN has several use cases in cellular vehicle-to-everything communications such as low-latency information exchange between vehicles and RAN intelligence controller (RIC). However, efficient data sharing between vehicles and RIC suffers from the challenges of signal loss due to mobility and dynamic channel conditions. In this regard, intelligent reflecting surfaces (IRS) have emerged as an intriguing concept of reconfigurable and smart environments to improve the performance of wireless communication systems. In the proposed research work, our main focus is on a multi-IRS aided single input single output system, where open RAN base stations (BS) convey information to a remote vehicular user. The transmitted signal is reflected by IRSs via multi-hop passive beamforming over pairwise line-of-sight links. To maximize the overall network sum-rate, we propose an IRS assignment method that allocates either a single IRS or multiple IRSs to each BS-user pair. In particular, the proposed algorithm consists of two stages, where in the first stage we perform$k$-means clustering to group the IRSs according to their location. In the next stage, for each group, we select the best IRS-assisted path (based on received signal strength) by transforming the original network into a trellis graph and using a trellis-search method. Simulation results show that the proposed technique outperforms existing IRS selection techniques in multiple IRS-enabled multi-hop communication systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".