Towards Reliable Communications in Intelligent Reflecting Surface-Aided Cell-Free MIMO Systems
Why this work is in the frame
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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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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 it