On the Maximum Useful Number of Receiver Antennas for MRC Diversity in Cochannel Interference and Noise
Bibliographic record
Abstract
The effect of noise on the maximum useful number of receiver antennas that can be deployed in a cochannel interference diversity system is examined. The long term signal- power-to-interference-plus-noise-power ratio (SINRP), the long term signal amplitude to the square root of interference plus noise power ratio (SAINPR), the average instantaneous signal-to- interference-plus-noise ratio (AISINR), and the average bit error rate (BER) of a maximal ratio combining (MRC) diversity system in the presence of multiple cochannel interferers and additive white Gaussian noise (AWGN) are evaluated when the desired user signal and the interfering user signals are independent, and each of them experiences correlated Ricean fading at the receiver antennas. The results show that a previous design rule which states that the performance of a fixed-size antenna array containing the maximum number of independent antennas cannot be significantly improved by adding more than one additional antenna, still applies when the interference dominates the noise. It is shown that the SINRP and SAINPR measures exhibit asymptotic limits as the number of correlated antennas increases. Simple expressions for these limits are derived and it is shown that these asymptotic limits are unchanged when noise is neglected.
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 | 0.004 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".