An Efficient Parallel Architecture for Implementing LST Decoding in MIMO Systems
Bibliographic record
Abstract
Recovering the symbols in a multiple-input multiple-output (MIMO) receiver is a computationally intensive process. The layered space-time (LST) algorithms provide a reasonable tradeoff between complexity and performance. Commercial digital signal processors (DSPs) have become a key component in many high-volume products such as cellular telephones. As an alternative to power-hungry DSPs, we propose to use a moderately parallel single-instruction stream, multiple-data stream (SIMD) coprocessor architecture, called DSP-RAM, to implement an LST MIMO receiver that offers high performance with relatively low power consumption. For a typical indoor wireless environment, a 100-MHz DSP-RAM can potentially provide more than ten times greater decoding throughput at the receiver of a (4,4) MIMO system compared with a conventional 720-MHz DSP. The DSP-RAM processor has been coded in a hardware description language (HDL) and synthesized for both available field-programmable gate arrays (FPGAs) and for a 0.18-mum CMOS standard cell implementation
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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".