Active IRS Design for RSMA-based Downlink URLLC Transmission
Bibliographic record
Abstract
Rate-splitting multiple access (RSMA) has been proposed as a flexible multiple access scheme for improving interference management in sixth-generation (6G) networks. In particular, the low latency facilitated by RSMA and its robustness against user mobility and imperfect channel state information make it an ideal candidate for the ultra-reliable and low-latency (URLLC) use case in 6G networks. However, since the common message in RSMA needs to be decoded by all the users, the achievable rate of the common message is determined by the user with the poorest channel quality. To overcome this bottleneck, an active intelligent reflecting surface (IRS) can be deployed to enhance the achievable rate of the common stream. However, this comes at the expense of additional power consumption due to the active IRS. In this paper, we consider an active IRS-aided RSMA-based downlink URLLC system and study the resource allocation design for minimization of the power consumption of the base station and the active IRS under quality-of-service constraints for the URLLC users. Our simulation results reveal that active IRSs yield a lower overall power consumption and require a smaller surface size compared to passive IRSs in RSMA-based URLLC systems. Moreover, we show that active IRS-aided RSMA systems consume less power than active IRS-aided space division multiple access (SDMA) systems.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".