Deep Learning Decoder for MIMO Communications with Impulsive Noise
Bibliographic record
Abstract
In this paper we consider signal detection in multiple-input-multiple-output (MIMO) systems with an impulsive noise channel. The existing, near optimal, sphere decoder (SD) achieves good performance, however, the computational complexity is directly related to the number of nodes visited during the tree search and the signal-to-noise ratio (SNR). Using neural network techniques, a Deep Learning Detector (DLD) is proposed. The DLD method detects signals transmitted in an impulsive noise channel, after an off-line training phase. The detection process of DLD has lower complexity than the average SD complexity, while exhibiting good performance. What is even more interesting is that the computational complexity of DLD is constant across SNR, in contrast to the SD detectors, which have an exponential complexity across the SNR. This constant complexity could be very helpful when implementing a detector in practice because it could allow for better optimization of resources. To evaluate the performance of our proposed method we have used a low level simulator that generates a fairly accurate model of a MIMO system with an impulsive noise channel. The complexity analysis and simulation results validate the arguments presented in this paper.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".