Quadrature Spatial Modulation in MIMO Cognitive Radio Systems With Imperfect Channel Estimation and Limited Feedback
Bibliographic record
Abstract
This paper studies the recent novel multiple-input multiple-output transmission technique called quadrature spatial modulation (QSM), in underlay cognitive radio (CR) systems. In particular, a multi-antenna secondary transmitter (ST) communicates with a multi-antenna secondary receiver (SR) in the presence of a primary receiver (PR). Considering only the statistical knowledge of the ST-PR channel gain, the QSM-CR scheme is investigated using a mean value (MV)-based power allocation strategy referred to as MV-based scheme. Furthermore, assuming that the ST-PR channel gain is perfectly known, the QSM-CR scheme is investigated using a power allocation method based on instantaneous channel state information (CSI), referred to as CSI-based scheme. In each scheme, considering imperfect ST-SR channel estimation, we study the secondary system performance, where closed-form expressions for the average pairwise error probability (P̅E̅P̅) are derived over Rayleigh fading channels. A tight upper bounded average bit error rate is obtained using the derived P̅E̅P̅ expression. Moreover, simple approximate expressions are obtained to get insights on the system diversity and channel estimation errors' effects. Numerical results, which match with simulations, illustrate the robustness of QSM in enhancing the overall system performance in the presence of estimation errors.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".