Imperfect CSI and Improper Gaussian Noise Effects on SSK: Optimal Detection and Error Analysis
Bibliographic record
Abstract
Space shift keying (SSK) has many advantages through its unique transmission manner as compared to other multiple-input multiple-output (MIMO) techniques. Nevertheless, the practicality of SSK in the presence of real-time imperfections such as channel estimation errors and hardware impairments (HWIs) is still an open research problem. On the other hand, the effects of HWIs are assumed as zero-mean circularly-symmetric complex Gaussian random variable (RV) in the literature. However, this model does not reflect the asymmetric characteristics of different HWIs. Therefore, the aim of this paper is to shed light on the joint effect of improper Gaussian noise (IGN) and imperfect channel state information (ICSI) on the performance of SSK receiver. Particularly, an optimal maximum likelihood (ML) detector is designed, and pairwise error probability (PEP) expression is derived. Additionally, an exact closed-form Cramer-Rao bound expression is calculated for evaluating the channel estimation accuracy under the effect of IGN. The results obtained by using computer simulations prove that the proposed optimal detector is superior to the traditional ML detector in the presence of IGN and ICSI.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".