Asymptotic SEP analysis for correlated large MIMO channels with ZF-DF detection
Bibliographic record
Abstract
In this paper, we consider correlated multiple-antenna communication systems having M transmitter antennas and N receiver antennas (M ≤ N) with a zero-forcing (ZF) decision-feedback (DF) detector. We assume that the full knowledge of channel state information is available at the receiver and only the first- and the second-order channel statistics are known at the transmitter. By scaling up the antenna array size of both terminals without bound for such multiple-antenna systems, we propose a novel method based on the Szegö's theorem and the well-known limit limx→∞(1 + 1/x)x = e to analyze the asymptotic behaviour on the error performance of an equal-diagonal QRS precoded large MIMO system when employing an abstract Toeplitz correlation model. This new approach bears a simple expression with a fast convergence rate and thus, is efficient and effective for error performance evaluation. Then, the impact of channel correlation on the error performance is studied for different correlation coefficients. In addition, an explanation of this approach in terms of the entropy power of the channel is also provided. Finally, computer simulations are carried out to verify our analysis in comparison with a uniform power allocation strategy.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".