Rate Maximization and Mode Selection for RDARS-Assisted MIMO Communications With Perfect and Imperfect CSI
Bibliographic record
Abstract
Reconfigurable distributed antenna and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to perform flexibly either in the reflection mode, like the traditional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, we investigate a dynamic RDARS-aided multiple-input multiple-output communication system, where the optimal configuration of the elements operating in connection mode can provide additional selection gain. Considering the theoretical and practical significances, we address the achievable rate maximization problem by jointly optimizing the mode selection, transmit power allocation and passive beamforming under both perfect and imperfect channel state information (CSI) cases. Due to the involvement of the mode selection design of RDARS, the problem is more challenging than those of the traditional RIS-aided systems with fixed reflection operation. For perfect CSI case, by investigating the inherent properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity and then extend the proposed algorithm to the general multi-user multi-RDARS scenario. Additionally, we find interesting insights about the mode selection of RDARS in a special scenario with a single-antenna user. The result shows that the RDARS elements leading to the largest distribution gain should be selected to operate in connection mode for the rate maximization. For imperfect CSI case, we develop an efficient alternative direction method of multipliers-based AO algorithm. Numerical results show that RDARS-assisted system outperforms the passive-RIS assisted system and DAS under both perfect and imperfect CSI scenarios with promising reflection, distribution and selection gains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".