Manifold Optimization Empowered Two-Timescale Channel Estimation for RIS-Assisted Systems
Bibliographic record
Abstract
In this paper, we propose a manifold optimization empowered two-timescale channel estimation (MO-TTCE) scheme for reconfigurable intelligent surface (RIS)-assisted multiuser millimeter wave massive MIMO systems. Since the base station (BS) and RIS remain relatively stationary while the user equipments (UEs) are mobile, the coherence time of the RIS-BS channel is significantly longer than that of the UErelated channels, namely the UE-RIS and UE-BS channels. Therefore, it is sufficient to estimate the RIS-BS channel only once in the large timescale, while frequently estimating the UErelated channels in the small timescale. We leverage the sparse and low-rank properties of the RIS-BS channel and transform the channel estimation problem into a series of sparse lowrank matrix recovery (SLRMR) problems, specifically ℓ1-norm regularized constrained optimization problems with the feasible region being a complex bounded-rank (CBR) matrix set. To ensure the differentiability of the objective function, we employ a differentiable Huber-γ function as a substitute for the ℓ1-norm. To handle the non-convex nature of the CBR matrix set, we treat the complex fixed-rank (CFR) matrix set as a CFR manifold and consider the CBR matrix set as a collection of CFR manifolds, thereby employing manifold optimization techniques to solve the problem. Furthermore, in the small timescale, we utilize the downlink pilot transmission and uplink feedback scheme to simultaneously estimate all UE-RIS channels. Simulation results show that the proposed MO-TTCE scheme can enhance the accuracy of channel estimation and reduce pilot overhead.
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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".