Low Complexity Joint Detection and Estimation for MIMO RIS-ISAC Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrated sensing and communications (ISAC) and reconfigurable intelligent surfaces (RIS) have been identified as key technologies in the realization of next-generation wireless networks. ISAC provides spectral and hardware efficiency by integrating the communications and sensing functionalities into one system, whilst RIS is used to improve signal transmission. In this letter, a low-cost RIS-ISAC receiver is proposed for the first time in a multiple-input multiple-output (MIMO) system. A search-tree is deployed to detect the MIMO RIS-ISAC transmitted communication signals. Next, the K-best algorithm is proposed to reduce the computational complexity of maximum likelihood (ML) detection while achieving near-ML bit-error rate performance. Additionally, the minimum mean-squared error estimation technique is adopted to estimate the reflection coefficients of a nearby target. Computational complexity analysis and Monte Carlo simulations are provided to support the findings.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it