Initialization of an Iterative Low-Complexity Method for Signal Precoding in MM-Wave Massive MIMO Systems
Bibliographic record
Abstract
In the last few years, huge interest has been directed towards research in wireless communications technology, notably at the level of the recently born massive MIMO systems.In such systems, the function of precoding at the base station (BS) plays a central goal in guaranteeing reliable downlink transmission.This paper aims to suggest a new low complexity linear precoding algorithm that can provide enhanced performance for downlink mm-wave massive MIMO systems.For this end, a first iterative solution is briefly computed by the Jacobi (Jac) method and then provided as an initialization for the known iterative symmetric successive over relaxation (SSOR) algorithm.This developed iterative way reduces the complexity by one order of magnitude compared with that of the zero forcing (ZF) near-optimal precoding, which relies on direct calculation of a large inverse matrix.In addition, to prove the performance of the new proposed Jac-SSOR iterative algorithm compared with its origin versions, some benchmarking simulations have been carried out in adequate typical scenario.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".