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Resource Allocation for RIS-Aided Cell-Free Massive MIMO-URLLC Network
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DOI：10.1109/TGCN.2026.3655209.png)
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Abstract
En 中文
Reconfigurable intelligent surface (RIS) have emerged as a transformative technology to enhance the performance of Internet-of-Things devices in service dead zones within cell-free massive multiple-input multiple-output (CFmMIMO) network, particularly in ultra-reliable and low-latency communication (URLLC) scenarios. Consequently, this paper investigates the rate performance of a RIS-aided CFmMIMO-URLLC network over Rician fading channel. First, we propose a low-overhead channel estimator and subsequently derive closed-form expressions for the downlink achievable rate under maximum ratio transmission (MRT) and zero-forcing (ZF) precoding schemes. According to these analytical expressions, we formulate a resource allocation problem aimed at maximizing the sum achievable rate by jointly optimizing the power control coefficient of base station and the phase shift of RIS. To tackle this non-convex problem, we propose an alternating optimization (AO) algorithm based on path-following method to approximate the complicated objective function as a logarithmic function. The resulting problem can be further decomposed into two subproblems, which are solved using an iterative optimization framework incorporating successive convex approximation and semidefinite relaxation techniques. Extensive numerical simulations validate the accuracy of the derived closed-form expressions and highlight the superior performance of ZF precoding in the short-packet regime. Finally, the proposed AO algorithm demonstrates significant improvements in rate performance for both MRT and ZF precoding schemes compared to several benchmark algorithms.
Keywords:
Reconfigurable intelligent surface
cell-free massive MIMO
ultra-reliable and low-latency communication
resource allocation
maximum ratio transmission
zero-forcing
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6.7
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1.3K
Citations:
4.3K
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