A pipelined scalable high-throughput implementation of a near-ML K-best complex lattice decoder
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a practical pipelined K-best lattice decoder featuring efficient operation over infinite complex lattices is proposed. This feature is a key element that enables it to operate at a significantly lower complexity than currently reported schemes. The main innovation is a simple means of expanding/visiting the intermediate nodes of the search tree on-demand, rather than exhaustively or approximately, and also directly within the complex-domain framework. In addition, a new distributed sorting scheme is developed to keep track of the best candidates at each search phase; the combined expansion and sorting cores are able to find the K best candidates in just K clock cycles. Its support of unbounded infinite lattice decoding distinguishes our work from previous K-best strategies and also allows its complexity to scale sub-linearly with modulation order. Since the expansion and sorting cores cooperate on a data-driven basis, the architecture is well-suited for a pipelined parallel VLSI implementation of the proposed K-best lattice decoder. Comparative results demonstrating the promising performance, complexity and latency profiles of our proposal are provided in the context of the 4x4 MIMO detection problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it