Grant-Free Random Access for RIS-Aided Machine-Type Communication
Bibliographic record
Abstract
The rapid growth of Internet of Things (IoT) applications, such as smart cities and industrial automation, necessitates efficient massive machine-type communication (mMTC) solutions for sixth-generation (6G) networks. Traditional access protocols struggle to accommodate many devices with sporadic activity and low data volumes, leading to increased latency and collisions. This paper proposes a novel grant-free random access (RA) protocol that leverages Reconfigurable Intelligent Surfaces (RIS) to enhance connectivity and channel conditions in mMTC scenarios. The protocol consists of two stages: first, the base station transmits downlink (DL) pilots while the RIS sweeps its reflection configurations, enabling devices to identify optimal transmission opportunities. In the second stage, devices utilize these opportunities to transmit data, minimizing collisions and improving throughput. By employing a predefined codebook of reflection configurations, previously optimized to thoroughly scan the covered space in a few rounds of multiple narrow beams, the protocol reduces overhead and meets a maximum latency constraint of 20 ms under certain reliability constraints, demonstrating significant performance improvements over existing methods. This approach enhances network efficiency while supporting a vast number of devices and encourages the deployment of RIS technology in future wireless communication systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.007 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".