Data Transmission and Reception in Spatial Modulation MIMO Wireless Systems and Analysis in Nakagami-m Fading Channels
Bibliographic record
Abstract
This research paper provides binary data transmission in spatial modulation (SM) multiple input multiple output (MIMO) wireless systems, its reception in Nakagami-m fading channel and its analysis by pairwise error probability.In spatial modulation MIMO systems data is transmitted through the selected antenna via its index where data bits are sent via Nakagamim fading channel whenever there is large cluster of multipath waves and large delay period.In the receiver data bits are detected using optimal detection or suboptimal detection from the corresponding antenna index where data bits are separately detected or simultaneously detected.Simulations are performed in matrix laboratory (MATLAB) for different values of modulation order M, number of transmitting antennas Nt, number of receiving antennas Nr and shape parameter m or the fading parameter.Results obtained for simulation of spatial modulation MIMO system shows that the performance in terms of pairwise error probability degrades as the order of modulation increases.However, the fact is that in spatial modulation MIMO system since antenna index is selected for transmission, the number of radio frequency (RF) chain decreases which results in reducing hardware complexity.The obtained results can be useful for research perspective for 5G and 6G wireless systems to cater the requirements of data transmission and reception.
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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".