Efficient V2I Communication via IRS-Enhanced MIMO Backscatter With Non-Linear Detection
Bibliographic record
Abstract
This paper investigates an Intelligent Reflecting Surface (IRS)-enhanced vehicle-to-infrastructure (V2I) multiple input multiple output (MIMO) backscatter communication network. In this network, multiple IRSs send roadside information to a multi-antenna reader using the backscatter technique, while the inevitable self-interference at the reader is taken into account. To maximize the introduced system’s weighted sum rate, we propose an optimization scheme based on minimum mean square error with successive interference cancellation (MMSE-SIC). This scheme jointly optimizes the reader’s detection matrix, beamforming vector, and IRS reflection coefficients. The formulated non-convex problem is tackled using a block coordinate descent (BCD) algorithm combined with successive convex approximation (SCA) and semi-definite relaxation (SDR) methods. The proposed framework is compared with other schemes including the linear detection technique, highlighting the trade-off between performance and computational complexity. The study provides insights into the impact of key parameters, such as the reader’s transmit power and the number of IRS elements, on system performance. Furthermore, our findings lay the groundwork for future exploration of more effective transmission approaches in IRS-enhanced V2I backscatter communication networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".