Towards Reliable Communications in Intelligent Reflecting Surface-Aided Cell-Free MIMO Systems
Bibliographic record
Abstract
Intelligent reflecting surface (IRS) and cell-free multiple-input multiple-output (CF-MIMO) systems are two promising multi-antenna technologies for the fifth generation and beyond (B5G) wireless communication systems. In this paper, we formulate a joint phase shift control and beamforming opti-mization problem to maximize the aggregate throughput subject to the reliability constraint of the users in an IRS-aided CF-MIMO system. We propose an alternating optimization (AO)-based algorithm, in which the joint problem is decomposed into a phase shift control subproblem and a beamforming subproblem. For the phase shift control subproblem, we propose a complex gradient descent (CGD)-based algorithm, which tackles the unit-modulus constraint and guarantees the aggregate throughput to be monotonic increasing in each iteration. We then propose a difference of convex programming (DCP)-based algorithm for beamforming optimization. Simulation results show that the proposed AO-based algorithm achieves an aggregate throughput that is 53.8% and 25.1% higher than the cellular MIMO system with zero-forcing beamformer and the IRS-aided CF-MIMO system with random phase shift control, respectively. Moreover, the reliability requirements of the users are satisfied with the proposed AO-based algorithm. Our results also demonstrate that the proposed algorithm improves the minimum throughput of the users and reduces the standard deviation of the throughput distribution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
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".