Maximizing Sum-Rate in Holographic Active RIS-Aided Uplink Near-Field Communications
Bibliographic record
Abstract
This work proposes the integration of the holographic active reconfigurable intelligent surface (HARIS) into a multi-user uplink near-field-driven wireless communication system. In order to provide efficient resource utilization, a sumrate maximization problem is formulated, where the equalizer design, the power allocation at each user, and the HARIS phase profile are jointly optimized under the strict constraint of QoS requirement and limited power budget at each user and HARIS. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR) to simultaneously optimize the equalizer at the BS, beamforming at the HARIS, and power allocation at each user. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum quality of service (QoS) constraint corresponding to each user, maximum receive power at the base station (BS), and maximum amplification factor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".