Low Complexity and Fast Processing Algorithms for V2I Massive MIMO Uplink Detection
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Bibliographic record
Abstract
The fast development of intelligent transport systems requires high-rate communications, high energy efficiency, and low latency. One promising solution to meet the requirements is to adopt the massive multiple-input multiple-output (MIMO) technique. The massive MIMO architecture is attractive to multiple vehicles on the road for vehicle-to-infrastructure access as large-scale antennas can be deployed at the roadside unit. Besides, massive MIMO systems can significantly improve the system spectrum efficiency and energy efficiency. However, the benefits are achieved at the cost of high computational complexity and long processing delay even with linear detection methods. In this paper, we propose low complexity and fast processing algorithms to address those issues. The proposed schemes transform the large-scale matrix inverse problems into solving linear equations. We then introduce iterative methods to solve linear equations. To speed up the updating process in iterative method, we utilize the properties of block matrix, and perform the updating process on a small size block independently. The independent processing progress can be paralleled, which greatly reduces the overall processing time. We also evaluate the performance of the proposed schemes in terms of the probability that the convergence conditions are met, and the system bit error rate. The results show that the proposed schemes achieve good system performance but at low complexity and latency.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it