New Hybrid Precoding for mmWave MIMO Systems: LADR and DALR Architectures
Bibliographic record
Abstract
Hybrid precoding for fully-connected architectures (FA) delivers superior performance in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems but comes at the cost of significantly higher complexity compared to sub-connected architectures (SA). This paper introduces two new sub-connected hybrid precoding architectures: localized-antennas distributed-RF (LADR) and distributed-antennas localized-RF (DALR), designed to balance the trade-off between performance and complexity. Both architectures divide transmitter antennas and RF chains into two groups, which are either distributed or localized. In LADR, localized antenna groups are connected to distributed RF chain groups, providing high beamforming precision, making it well-suited for dense urban deployments where performance demands are stringent. In contrast, DALR connects distributed antenna groups to localized RF chain groups, offering beamforming with lower precision compared to LADR, making it better suited for large-scale networks, such as rural or wide-area applications, where broader coverage and scalability are prioritized over high precision. The hybrid precoding is optimized and solved iteratively by decomposing the problem into two independent subproblems, referred to as the odd and even subproblems. Simulation results demonstrate that the proposed architectures achieve performance close to FA, while reducing the number of phase shifters by 50% and lowering computational complexity to$\mathcal {O}(N_{t})$, compared to the$\mathcal {O}(N_{t}^{2})$complexity of traditional FA designs, where$N_{t}$is the number of transmitter antennas. Furthermore, the proposed architectures outperform traditional SA by approximately 3 dB with only a slight increase in complexity. The results also indicate that LADR offers slightly better performance than DALR when the number of data streams is high due to its superior beamforming capability, while both architectures perform similarly when the number of data streams is low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".