Low Complexity and Fast Processing Algorithms for V2I Massive MIMO Uplink Detection
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".