Hybrid Precoding for mmWave MIMO Systems With Overlapped Subarray Architecture
Bibliographic record
Abstract
Traditional precoding is incompatible with millimetre wave (mmWave) multiple input multiple output (MIMO) systems due to hardware costs and power consumption.As a result, hybrid precoding is being considered as a promising technology for balancing hardware complexity and system performance.This paper proposes a hybrid precoding technique for overlapped subarrays (OSA) architecture in mmWave MIMO systems.It exploits the OSA's structure to decompose the hybrid precoding problem into a series of /2 subproblems and then solves them iteratively, where and are the numbers of RF chains and transmitted data streams, respectively.First, the proposed scheme determines the analog and digital precoding submatrices of each OSA iteratively and then constructs the analog and digital precoding matrices of the whole OSA architecture.The digital precoding submatrix of each OSA is determined from each vector in the OSA submatrix.The results show that the proposed hybrid precoding for the OSA architecture outperforms the successive interference cancellation (SIC)-based hybrid precoding for non-overlapped SA (NOSA) and provides performance close to the fullyconnected (FC) spatially sparse hybrid precoding, despite requiring less hardware and computational complexities.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".