Adaptive spatial modulation for spectrally-efficient MIMO systems
Bibliographic record
Abstract
Using spatial modulation (SM) jointly with adaptive modulation (AM), we propose a low-complexity and spectrally-efficient transmission scheme in a multiple-input multiple-output (MIMO) system. While in the conventional SM technique a fixed data rate is achieved, the proposed adaptive spatial modulation (ASM) technique is throughput-optimized by taking advantage of the wireless channel variations in order to increase the spectral efficiency of SM. ASM has been previously studied in [1] in order to improve the average bit error rate (ABER) performance of SM while only providing a fixed data rate. On the other hand, the ASM technique is introduced in this paper in order to achieve high data rates while keeping the ABER below a certain threshold. We propose two variations of ASM compromising between the spectral efficiency and the error performance. The ABER and the average spectral performance results of both variations are presented via Monte-Carlo simulations and confirmed with analytical results including asymptotic performance bounds on the ABER. These results show that the proposed ASM techniques come with a considerable spectral efficiency gain compared to SM while only requiring a limited feedback from the receiver.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".