On the Performance of Rate Splitting Multiple Access for ISAC in Device-to-Multi-Device IoT Communications
Bibliographic record
Abstract
In this paper, we analyze the performance of rate splitting multiple access (RSMA) technique for a multi-device communication system applying integrated sensing and communication (ISAC) to alleviate the problem of overlapping spectrum of radar signal and communication frequency bands. The system includes a cooperative access point (AP) which serves as a sensing node and a decode-and-forward (DF) relay to support the communication between a mobile device (MD) and multiple Internet-of-Things devices (IoDs). Assuming Nakagami fading channels, we provide an extensive analytical framework to evaluate the dual functionalities of the system considering various scenarios with different assumptions on blocklength, channel state information (CSI), and successive interference cancellation (SIC). In other words, we consider both infinite and finite blocklength transmissions under practical impairments including imperfect CSI and SIC. We investigate the outage probability (OP), and ergodic sum rate assuming infinite blocklength, while the block error rate (BLER), and goodput are analyzed in the finite blocklength regime. The closed-form and asymptotic expressions for the OP and BLER are presented. In addition, to evaluate the sensing performance, we derive the closed-form expressions of the false alarm and detection probabilities. Through the simulation results, we validate our analysis and delve into the impacts of various system parameters including transmit power, Nakagami shaping parameter, CSI error, SIC imperfection, the number of devices, and sensing threshold. Further, we observe that the proposed RSMA-based ISAC system provides higher ergodic sum rates compared to non-orthogonal multiple access (NOMA) both in the presence and absence of practical impairments.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".