Robust Hybrid Transmitter Design for DAoSA With Low-Resolution Hardware and Imperfect CSI
Bibliographic record
Abstract
Dynamic array-of-subarrays (DAoSA) hybrid precoding architectures have shown great potential in reducing power consumption while meeting data rate requirements in THz massive Multiple-Input Multiple-Output (mMIMO) systems. These structures employ a network of dynamic switches to adapt the connections between available RF chains and subarrays, based on current channel state information (CSI). While ongoing research often assumes ideal hardware components, i.e., phase shifters (PS) and digital-to-analog converters (DAC), along with perfect CSI, deviations from these assumptions may entail significant loss in performance. In this paper, we introduce a new robust transmitter design methodology for DAoSA, with the objective of optimizing sum rate while mitigating the adverse effects of low-resolution hardware and imperfect CSI. Leveraging a Gaussian model for the channel uncertainties, we formulate the problem as optimizing a worst-case sum rate expression with respect to the system parameters, i.e., analog and baseband precoder matrices, switch network matrix, and bit allocation for DAC, subject to practical constraints on transmit power budget and minimum transmission rate. To address this complex optimization problem, we devise a novel penalty dual decomposition (PDD) algorithm that can effectively handle difficulties posed by the coupling terms within the constraints of the original problem. The performance of the proposed method for robust hybrid DAoSA transmitter design is thoroughly evaluated by means of numerical analysis. The results demonstrate that in the presence of low-resolution hardware components, imperfect CSI, and total power constraint, the proposed design method leads to improved sum rate when compared to non-robust and other robust design approaches.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".