Space-Time Block Coding-Assisted Fluid Antenna System for Electromagnetic Interference Mitigation in Wireless Communication Systems
Bibliographic record
Abstract
Electromagnetic interference (EMI) still poses a serious threat to reliable wireless communication, particularly in crowded and hostile electromagnetic environments. While space-time block coding (STBC) and other conventional diversity techniques have been extensively employed to mitigate multipath fading, their ability to mitigate EMI is inherently limited, especially when interference uniformly affects every component of the antenna. The spectral efficiency of Fluid Antenna (FA)-enabled Multiple-Input Multiple-Output (MIMO) systems can be significantly enhanced by employing Index Modulation (IM). However, current FA-enabled IM (FAIM)-aided MIMO systems suffer from considerable performance degradation due to strong spatial correlation in the wireless channel, which is caused by the dense port distribution of the FA. In this paper, we propose an effective approach that integrates a Fluid Antenna System (FAS) with STBC to enhance system robustness in the presence of EMI. Simulation results show that integrating an FAS into a dual-antenna receiver employing STBC significantly improves spectral efficiency and reliability under EMI. At low signal-to-noise ratios (SNRs), the presence of EMI reduces Bit Error Rate (BER) performance by more than 12% when compared to the traditional arrangement without EMI. In contrast to the system affected by EMI without FAS, the BER curve closely resembles the scenario without EMI when FAS selection is used, resulting in a ~10% BER reduction at Eb/N0= 6 dB. Similarly, the achievable rate bridges the performance gap caused by EMI by improving by more than 1.5 bps/Hz over the SNR range. These findings confirm that FAS is effective in reducing EMI and improving communication in unfriendly settings.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".