A Practical Algorithm for Realizing GDFE Precoder for Multiuser MIMO Systems
Bibliographic record
Abstract
It is well known that multiuser multiple-input multiple-output (MU-MIMO) systems can achieve superior data rates compared to single user MIMO links. The improvement in data rates offered by the MU-MIMO systems is dependent on the design of precoding scheme for the broadcast channel (BC). A precoding scheme based on generalized decision feedback equalizer (GDFE) is known to achieve MIMO BC capacity. However, GDFE precoder suffers from huge computational complexity and is not suitable for practical systems. Much of computational cost of implementing a GDFE precoder can be attributed to its reliance on the covariance matrix corresponding to the ``least favorable noise'', which has prohibitive computational complexity. In this paper, we provide an alternative framework for realizing a GDFE precoder, which avoids the need to compute ``least favorable noise''. While maintaining capacity optimality of the GDFE precoder, the proposed algorithm has significantly lower complexity that is comparable to other MU-MIMO precoding schemes. Additionally, the proposed algorithm provides a useful tradeoff between desired complexity and performance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".