A novel parallel detection architecture for modular MIMO receivers
Bibliographic record
Abstract
Recent advances in information theory show that, given the right propagation environment, an arbitrarily large channel capacity can be obtained if multielement antenna arrays are used at both the transmitter and the receiver. In such schema, parallel detection is advantageous, since it is faster than sequential detection, it does not require power ordering at the receiver and it reduces the system latency. Previous work has proposed a parallel detector (PD) based on successive symbols approximation. Parallel detectors found in the literature suffer from high computational complexity. We first propose a modified parallel detection (MPD) algorithm that reduces the system computational complexity considerably. The algorithm can be realized using a parallel modular architecture, which consists of a set of elementary sub-detectors. The data flow between the sub-detectors is significantly reduced. The MPD also allows performance improvements when it is used as a refinement to sequential vertical Bell Labs layered space-time algorithm (V-BLAST). However, both PD and MPD algorithms do not generally perform as well as sequential V-BLAST. Thus, secondly, we suggest another parallel detection architecture. This is based on a minimum mean square error (MMSE) criterion in its first stage and on weighted parallel interference cancellation in its second stage. Simulation results show that our parallel detector performs better than sequential V-BLAST for the same order of complexity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".