Adaptive spatial modulation for spectrally-efficient MIMO 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
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.
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.
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 | 0.000 |
| 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) | 0.000 | 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