Near-Field Beamforming Optimization for Holographic XL-MIMO Multiuser Systems
Bibliographic record
Abstract
Extremely large-scale multiple-input multiple-output (XL-MIMO) communications and ultra-high frequency bands are both potential enablers for satisfying extreme performance requirements of future wireless systems. Thanks to low hardware cost and power consumption, holographic metasurface antennas (HMAs) operating at high frequencies have recently emerged as an effective realization of large-scale antenna arrays, leading to greatly enlarged near-field region. In this paper, we investigate a power-efficient HMA-based near-field downlink multiuser system, where three different HMA-based arrays are considered. Specifically, we aim to minimize the total transmit power for each HMA-based array while maintaining the signal to interference plus noise ratio (SINR) constraint of each user by jointly optimizing the digital transmit precoder and the analog HMA weighting matrix. In the special single-user scenario, we validate that the original optimization problem can be decomposed into several independent subproblems each corresponding to a single HMA microstrip, whose optimal solution can be obtained by the successive convex approximation (SCA) based method. It is also revealed that the HMA-based array is capable of achieving near-field beam focusing. In the general multiuser scenario, we develop an efficient SCA-alternating direction method of multipliers (ADMM) based alternating optimization (AO) algorithm to tackle the intractable optimization problem, where the digital precoders and the HMA weighting matrix are iteratively optimized in an alternating manner. Numerical results demonstrate the superior performance of our proposed algorithms over existing benchmark schemes. It is also shown that the HMA-based array attains lower hardware overhead and power consumption as compared to the conventional hybrid array.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".