Detection-First or Estimation-First: A Study on Interference Cancellation for Overdetermined MIMO RIS-assisted ISAC Systems
Bibliographic record
Abstract
Efficient symbol detection and sensing parameter estimation in integrated sensing and communications (ISAC) systems are essential to ensure precise sensing while maintaining reliable communication, enabling effective integration of both functionalities in future wireless networks. Since the ISAC base station receives both communication and sensing signals simultaneously, most existing approaches employ a sequential method to extract communication and sensing information. This study investigates the performance of two interference cancellation (IC) based strategies in multi-input multi-output reconfigurable intelligent surfaces-assisted ISAC systems: detection-first (D-E), where symbols are detected prior to sensing parameter estimation, and estimation-first (E-D), where sensing parameters are estimated first, followed by symbol detection from the residual signal. A mathematical formulation is provided to implement the E-D approach. Moreover, a novel adaptive K-best algorithm (AKBA) based method is proposed for symbol detection, while iterative successive IC-based D-E and E-D approaches are introduced to further enhance the detection and estimation performance. The results indicate that the E-D approach is preferable when an iterative method is used. On the other hand, for the non-iterative approach, D-E outperforms E-D in some SINR regions, while E-D performs better in others. Furthermore, iterative processing significantly enhances symbol detection and sensing accuracy at the expense of computational complexity. The proposed AKBA algorithm reduces the computational complexity while maintaining near-optimal symbol detection performance. These results highlight the practical applicability of the proposed approaches by addressing the trade-off between performance and computational complexity.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".