A low complexity soft-output data detection scheme based on Jacobi method for massive MIMO uplink transmission
Bibliographic record
Abstract
In massive multiple-input multiple-output (MIMO) systems, linear minimum mean-square error (MMSE) detection can achieve near-optimal performance. However, it suffers from high computational complexity due to the involvement of matrix inversion. This issue becomes severer when user number (U) and receive antenna number (S) increase. Existing approaches such as Neumann series expansion method, Gauss-Seidel and Jacobi methods, can partly address this issue by approaching the matrix inversion with matrix multiplications or solving linear equations with iterative methods, respectively. However, matrix multiplications and the initialization for iterative methods are still costly. In this paper, we propose a further improved Jacobi method based soft-output massive MIMO detection scheme. The contributions include the use of matrix-vector product and a new approach to compute the log likelihood ratio (LLR). By using the matrix-vector product, the overall computational complexity is reduced from O (B × U2) to O(B × U). The new approach uses the noise-plus-interference (NPI) from the MMSE estimation, instead of using that from the first iteration. We then propose an approximation method to obtain the covariance of the NPI from MMSE estimation. Finally, we demonstrate through numerical simulations that the proposed scheme outperforms the existing schemes in terms of computational complexity and system bit error rate performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".