Multiantenna Spectrum Sensing Over Correlated Nakagami-m Channels With MRC and EGC Diversity Receptions
Bibliographic record
Abstract
Increasing number of antennas are closely packed in emerging multiantenna systems and correlation among them can no longer be ignored. In this paper, such a multiantenna spectrum sensing system is investigated considering dual, triple, four and up to infinite number of correlated antenna branches. Constant, arbitrary and exponential correlation among the antenna branches are considered. Closed form expressions for the detection probability, in terms of the confluent hypergeometric function, is derived assuming maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques in Nakagami-m multipath fading channel. Numerical results quantify the interbranch correlation that impacts the detector performance significantly. However, results also show that this effect could be compensated by employing the appropriate diversity combining technique and by increasing the diversity branches. Furthermore, we find that at high m values (Rician like channel), low false alarm probability and highly correlated environments, EGC which is a simpler scheme performs as good as MRC which is a more complex scheme.
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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 | 0.002 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".