Maximal-Ratio Combining Architectures and Performance With Channel Estimation Based on a Training Sequence
Bibliographic record
Abstract
Maximum-ratio combining (MRC) is a simple and effective combining scheme for adaptive antenna arrays to combat noise, fading, and to a certain degree, cochannel interference. However, it requires estimation of the spatial signature (i.e., the channel gain and phase at each antenna element) of the desired signal across the array. Assuming that this estimate is obtained by correlation using a known training sequence of K symbols embedded in the useful signal, we proceed to develop a fully analytical assessment of the impact of estimation error on the output signal-to-noise ratio (SNR) of the array. The originality of the approach revolves around the derivation of the distribution of the normalized SNR, that is the real SNR normalized to the ideal (i.e., perfect estimation) SNR. The end result is a set of distributions which can potentially reduce or in certain cases eliminate the need for simulation to determine certain design parameters such as array size, training sequence length, etc. These are then applied to find closed-form expressions for the outage probability and the error probability in differential phase-shift keying and quarternary phase-shift keying after training in uncorrelated Rayleigh fading.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".