Hybrid Precoding/Combining for mmWave MIMO Systems With Hybrid Array Architecture
Bibliographic record
Abstract
Utilizing hybrid precoding/combining for subarray (SA) architectures in millimeter-wave (mmWave) multi-input multi-output (MIMO) systems offers reduced hardware cost and power consumption when compared with that for full array (FA) architectures, although the spectral efficiency is lower. Hence, this paper introduces a new hybrid array (HA) architecture for mmWave MIMO systems, designed to achieve a balance between spectral efficiency, cost, and power consumption. Initially, the proposed HA architecture partitions the antennas into distinct subarrays. The number of these subarrays equals the count of radio frequency chains at the transmitter/receiver. These subarrays are subsequently organized into separate subsets referred to as groups. Ultimately, within each group, the antennas are connected with a corresponding group of radio frequency chains, employing a connection method similar to that of the FA architecture. Compared to the SA architecture, the proposed HA architecture provides higher spectral efficiency by exploiting spatial diversity through grouping of antennas. Furthermore, the HA architecture achieves cost and power reduction in comparison to the FA architecture by connecting a subset of antennas to each group of radio frequency chains. Two efficient iterative hybrid precoding/combining algorithms are also proposed, studied and compared for HA architecture mmWave MIMO system. During the design derivations, the proposed algorithms consider the block structure inherent in the analog precoding and combining matrices within the HA architecture. This consideration makes it possible to independently optimize hybrid precoding/combining for each group. Results show that the suggested HA hybrid design usingAlgorithm 2provides better performance than usingAlgorithm 1and both algorithms can achieve higher spectral efficiency than the conventional SA and FA hybrid designs while being less complex.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".