Fluid Antenna Index Modulation for MIMO Systems: Robust Transmission and Low-Complexity Detection
Bibliographic record
Abstract
The fluid antenna (FA) index modulation (IM)-enabled multiple-input multiple-output (MIMO) system, referred to as FA-IM, significantly improves spectral efficiency (SE) compared to conventional systems. However, the system does not account for spatial correlation, which significantly affects performance. To address this issue, this paper proposes an innovative FA grouping-based IM (FAG-IM) system by redesigning the mapping of bits to port indices. Specifically, FA ports are allocated into multiple groups, with each group independently performing port IM and symbol modulation. To enhance robustness against spatial correlation, a block grouping scheme is proposed, in which adjacent ports are assigned to the same group. Then, the upper bound of the average bit error probability (ABEP) for the proposed system is derived. Furthermore, exploiting the signal structure of the FAG-IM system, a low-complexity detector is developed within the approximate message passing (AMP) framework. The simulation results confirm that the proposed FAG-IM system outperforms existing systems, demonstrating excellent robustness against spatial correlation. Additionally, the results indicate that the proposed detector achieves superior performance with low complexity, thereby facilitating the implementation of the FAG-IM system.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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".