Dynamic Resource Allocation for Cell-Free Massive MIMO Networks With Critical Services Support

This paper presents a novel approach to resource allocation in Cell-Free Massive MIMO networks, with a particular focus on supporting Ultra-Reliable Low-Latency Communications (URLLC). Traditional solutions typically rely on successive refinements of an initial allocation, which leads to high computational complexity and limits their practicality in real-time environments, especially when addressing the heterogeneous requirements of different user equipment types. In contrast, the proposed approach introduces a holistic algorithm that computes the optimal resource allocation for both URLLC and Non-URLLC users without requiring successive refinements, ensuring a globally optimized solution is obtained directly. This optimization significantly enhances computational efficiency while preserving high spectral performance across the network. The proposed algorithms achieve better results when compared to the State-of-the-Art methods regarding average spectral efficiency and the 90%-likely spectral efficiency while optimizing both metrics simultaneously. Moreover, the computational complexity of the proposed algorithms is significantly lower than that found in related works, especially in scenarios with 5 pilots, where state-of-the-art algorithms are more than 35 times slower. This reduction in execution time highlights the potential of our models for real-time applications in dense network scenarios.