Enhanced Simulated Bifurcation for MIMO Detection
Bibliographic record
Abstract
Massive multiple-input, multiple-output (MIMO) systems enhance spectral efficiency, coverage, and reliability, making them essential for next-generation wireless networks. However, maximum likelihood (ML) detection in MIMO remains an NP-hard problem. This work explores CMOS-friendly Ising solvers, particularly simulated bifurcation, as alternative MIMO detection methods. We introduce a novel temperature schedule for ballistic simulated bifurcation (bSB), developing an enhanced variant, bSBG, to improve bit error rate (BER) performance. bSBG achieves faster convergence and mitigates the error floor issues present in bSB. Experimental results show that both bSB and bSBG outperform linear minimum mean squared error (LMMSE) and K-best detection by up to 12 dB and 4 dB, respectively, at a BER of 10−3for a 128×128 antenna configuration, while bSBG eliminates the error floor for 16×16 and 32×32 configurations. Additionally, bSBG and bSB achieve speedups of 7.2× and 9.2× over K-best, respectively, while running 7.5× slower than LMMSE but providing a 13 dB gain at a BER of 10−4. These results highlight bSBG as a promising candidate for efficient MIMO detection in large-scale systems.
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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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".