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Statistical CSI-Based Optimization for Uplink RIS-Aided Cell-Free Massive MIMO Systems
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DOI：10.1109/JIOT.2026.3666120.png)
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Abstract
En 中文
We address comprehensive optimization of uplink (UL) spectral efficiency (SE) and resource allocation in multiple reconfigurable intelligent surface (RIS)-aided cell-free massive multiple-input multiple-output (CF-mMIMO) systems. While integrating CF-mMIMO with RISs enhances SE, existing solutions assume ideal conditions or separately optimize access point (AP) clustering, large-scale fading decoding (LSFD), RIS phase shifts, and power allocation. To bridge these gaps, we propose a unified statistical channel state information (CSI)-based optimization (SCOP) framework that jointly optimizes AP clustering, LSFD, RIS phase-shift control, and UL power allocation to maximize the minimum UL SE. A closed-form (CF) SE expression is derived for maximum ratio (MR) combining, accounting for both direct and cascaded channels with spatially correlated Ricean fading. Leveraging statistical CSI significantly reduces real-time acquisition overhead while enabling robust and efficient UL transmission design. SCOP is solved via a multistep strategy: 1) for fixed power, an iterative algorithm computes a joint optimization parameter (JOP) vector representing a combination of AP clustering, LSFD, and RIS phase-shift parameters; 2) a CF solution updates power allocation; and 3) an alternating optimization (AO) jointly refines both. We also introduce a novel method to extract the optimal system parameters from the JOP. The simulation results show that, in a representative 60 APs, 30 users, and 4-RIS scenario, the proposed SCOP framework lifts the median UL SE from 2.4 to 3.9 bit/s/Hz (+ 65%) and more than doubles the bottom 5% rate, with similar 60%–120% gains in other setups.
Keywords:
Cell-free massive multiple-input multiple-output (CF-mMIMO)
max–min fairness (MMF)
power allocation
reconfigurable intelligent surface (RIS)
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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