# A Unified Two-Stage Generative Diffusion Framework for Channel Estimation and Port Selection in Multiuser MIMO-FAS arXiv: https://arxiv.org/abs/2605.29679 ยท HTML: https://mlsanity.com/paper/2605.29679/ ## Key facts - No citing papers in the map yet (data through 14 Oct 2026). - Placed in Diffusion for compression and wireless, one of 14 directions in Diffusion models. - Builds on 32 of its 46 references that are in the map. - First posted on arXiv 28 May 2026. ## Placed in - Diffusion models / [Diffusion for compression and wireless](https://mlsanity.com/diffusion/diffusion-for-compression-and-wireless/) ## Builds on (most cited first, 30 of 32) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022) - [Neural Ordinary Differential Equations](https://mlsanity.com/paper/1806.07366/) (2018) - [Improved Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2102.09672/) (2021) - [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022) - [Structured Denoising Diffusion Models in Discrete State-Spaces](https://mlsanity.com/paper/2107.03006/) (2021) - [Diffusion Posterior Sampling for General Noisy Inverse Problems](https://mlsanity.com/paper/2209.14687/) (2022) - [Building Normalizing Flows with Stochastic Interpolants](https://mlsanity.com/paper/2209.15571/) (2022) - [DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization](https://mlsanity.com/paper/2302.08224/) (2023) - [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019) - [Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications](https://mlsanity.com/paper/2111.12444/) (2021) - [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023) - [FlowDPS: Flow-Driven Posterior Sampling for Inverse Problems](https://mlsanity.com/paper/2503.08136/) (2025) - [A Tutorial on Extremely Large-Scale MIMO for 6G: Fundamentals, Signal Processing, and Applications](https://mlsanity.com/paper/2307.07340/) (2023) - [Fluid Antenna Systems](https://mlsanity.com/paper/2005.11561/) (2020) - [A Tutorial on Fluid Antenna System for 6G Networks: Encompassing Communication Theory, Optimization Methods and Hardware Designs](https://mlsanity.com/paper/2407.03449/) (2024) - [A New Analytical Approximation of the Fluid Antenna System Channel](https://mlsanity.com/paper/2203.09318/) (2022) - [Compressed Sensing Based Channel Estimation for Movable Antenna Communications](https://mlsanity.com/paper/2306.04333/) (2023) - [Successive Bayesian Reconstructor for Channel Estimation in Fluid Antenna Systems](https://mlsanity.com/paper/2312.06551/) (2023) - [Channel Estimation for Movable Antenna Communication Systems: A Framework Based on Compressed Sensing](https://mlsanity.com/paper/2312.06969/) (2023) - [Movable-Antenna Array Enhanced Beamforming: Achieving Full Array Gain with Null Steering](https://mlsanity.com/paper/2308.08787/) (2023) - [Globally Optimal Movable Antenna-Enhanced multiuser Communication: Discrete Antenna Positioning, Motion Power Consumption, and Imperfect CSI](https://mlsanity.com/paper/2408.15435/) (2024) - [Joint Beamforming and Antenna Position Optimization for Fluid Antenna-Assisted MU-MIMO Networks](https://mlsanity.com/paper/2503.04040/) (2025) - [Accurate and Fast Channel Estimation for Fluid Antenna Systems with Diffusion Models](https://mlsanity.com/paper/2505.04930/) (2025) - [Fluid Antenna with Linear MMSE Channel Estimation for Large-Scale Cellular Networks](https://mlsanity.com/paper/2212.08308/) (2022) - [Outage Performance of Fluid Antenna System (FAS)-aided Terahertz Communication Networks](https://mlsanity.com/paper/2301.08904/) (2023) - [Multiuser Communications with Movable-Antenna Base Station: Joint Antenna Positioning, Receive Combining, and Power Control](https://mlsanity.com/paper/2308.09512/) (2023) - [Antenna Positioning and Beamforming Design for Fluid-Antenna Enabled Multi-user Downlink Communications](https://mlsanity.com/paper/2311.03046/) (2023) - [Channel Estimation for FAS-assisted Multiuser mmWave Systems](https://mlsanity.com/paper/2311.11041/) (2023) - [Exploiting Movable Antennas in Multicast Communications](https://mlsanity.com/paper/2405.07281/) (2024) ## Abstract (arXiv) Fluid antenna systems (FAS) have emerged as a promising technology for next-generation wireless systems. However, practical multiuser multiple-input multiple-output FAS (MIMO-FAS) faces two inherently coupled challenges: acquiring accurate high-dimensional channel state information (CSI) from limited RF chains and solving the combinatorial port selection problem, where the effectiveness of the latter highly depends on the result of the former. In this paper, we propose a unified two-stage diffusion framework that formulates the joint task as a maximum-a-posteriori (MAP) inference problem and decomposes it into two sequential sampling stages through a plug-in approximation. For Stage I, a continuous flow-based diffusion model serves as a powerful implicit prior for 2D FAS channels, and a parallel guided generation scheme realizes approximate posterior sampling, enabling accurate multiuser channel recovery even under severely low sub-sampling ratios. For Stage II, a discrete diffusion model is trained to approximate the conditional port selection distribution by combining supervised learning on heuristic labels with reinforcement fine-tuning, effectively overcoming the local optima of conventional heuristic algorithms. Extensive simulations demonstrate that the proposed framework simultaneously achieves exceptional channel estimation accuracy and globally optimized port selection, substantially improving the minimum achievable rate.