Resource Allocation in User-Centric Cell-Free Massive MIMO URLLC Systems with Network Slicing | Zendy

Han Lu | Zendy; Weiwei Xia | Zendy; Weiwei Miao | Zendy; Mingxuan Zhang | Zendy; Feng Yan | Zendy; Lianfeng Shen | Zendy

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Resource Allocation in User-Centric Cell-Free Massive MIMO URLLC Systems with Network Slicing

Author(s) -

Han Lu,

Weiwei Xia,

Weiwei Miao,

Mingxuan Zhang,

Feng Yan,

Lianfeng Shen

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3591008

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

With the development of sixth-generation mobile communication technology (6G), ultra-reliable and low latency communication (URLLC) is vital for latency-sensitive applications. User-centric cell-free massive MIMO (CF-mMIMO) architectures meet these demands by removing cell boundaries and ensuring fair service for edge users. However, resource allocation in user-centric CF-mMIMO networks with URLLC constraints presents significant challenges. In this paper, we present a novel resource allocation scheme for network slicing based user-centric CF-mMIMO networks to maximize the achievable sum data rate while maintaining the quality of URLLC services. To solve the inherently non-convex optimization problem, the original problem is decomposed into two subproblems: the user-slice association problem, which optimizes the user-slice mapping variables, and the resource allocation problem, which optimizes the bandwidth allocation and power control coefficients. The user-slice association problem is addressed using the simulated annealing algorithm. For the resource allocation problem, a successive rate lower bound maximization algorithm is employed to obtain the convex lower bound of the data rate under short packet transmission, and the successive convex approximation (SCA) method is used to determine the optimal bandwidth and frequency allocation. Simulation results show that the proposed scheme improves the sum data rate by at least 10.24% and reduces the average transmission delay by at least 33.40% compared to the existing algorithms.

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