A low-complexity high-speed QR decomposition implementation for MIMO receivers
Bibliographic record
Abstract
QR decomposition (QRD) is an essential signal processing task for many MIMO signal detection schemes. However, decomposition of complex MIMO channel matrices with large dimensions leads to high computational complexity, and hence results in either large core area or low throughput. Moreover, for mobile communication applications that involve fast-varying channels, it is required to perform QR decomposition with low processing latency. In this paper, we propose a hybrid QRD scheme that uses a combination of multi-dimensional Givens rotations, Householder transformations and the conventional two-dimensional (2D) Givens rotations to both reduce the overall computational complexity and achieve higher execution parallelism. To prove the effectiveness of the proposed QRD scheme, a novel pipelined architecture is presented that uses un-rolled pipelined CORDIC processors iteratively to maximize throughput and resource utilization, while minimizing the gate count. The architectures of the main data processing modules, namely the 2D, Householder 3D and 4D/2D configurable pipelined CORDIC processors, are also presented. Synthesis results for a 4times4 MIMO detector in 0.13 mum CMOS process indicate that this QRD design computes a 4times4 complex R matrix and four updated 4times1 complex symbol vectors every 40 cycles, at a clock frequency of 270 MHz and requires 36 K gates. The proposed design achieves the lowest processing time and the highest throughput reported to-date for the same framework.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".