Architectures for Hybrid Precoding and Combining Techniques in Massive MIMO Systems Operating in the mmWave Band
Bibliographic record
Abstract
Hybrid precoding and combining techniques in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with various array architectures have attracted significant interest as a promising technology for the development of 6G wireless communication systems. This approach presents numerous advantages, including reduced complexity, cost, and power consumption, when compared to traditional analog precoding methods. In this chapter, we investigate hybrid precoding and combining techniques for massive MIMO systems operating in the millimeter-wave (mmWave) band, with a focus on different architectures, such as full array (FA), subarray (SA), and hybrid array (HA) architectures. We discuss the system model of each architecture. Additionally, we solve the hybrid precoding and combining optimization problem to maximize the spectral efficiency of each architecture. We then propose iterative hybrid precoding and combining algorithms for all architectures, as well as compare their performance to that of traditional hybrid design methods to demonstrate that the proposed algorithms achieve superior performance with lower complexity and hardware requirements.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".