## 1 Introduction  
To meet the growing demands of mobile network users, it is not enough to utilize larger bandwidths or density networks. In addition, advanced transmission techniques that increase spectral efficiency must be used. For currently deployed 5G networks and future 6G networks, such a technique is Massive Multiple-Input-Multiple-Output (M-MIMO) [1], which involves the use of multi-element antenna arrays that allow for the creation of narrow beams of radio signals directed toward individual users. This enables spatial multiplexing of users sharing the same time-frequency resources. Fifth-generation systems employing M-MIMO typically assume that the connection of one user is provided by a single base station (BS), which is referred to as a network-centric architecture. In such an architecture, radio signal coverage is uneven, with users closer to the BS receiving a much stronger signal than those farther away. As a result of this uneven Received Signal Strength (RSS) distribution, there is a significant disparity in throughput between users near the BS and those at the cell edge [2]. Therefore, for 6G systems, a novel, user-centric cell-free architecture combined with M-MIMO (UCCF M-MIMO) is being considered [3]. In a UCCF M-MIMO network, the concept of cells disappears, and following the so-called canonical definition in [4], the user can be served by all surrounding base stations. This effectively places the user in the center of a virtual cell, greatly reducing the throughput disparitiesamong different users. The deployment of UCCF M-MIMO is possible using the open Radio Access Network (RAN) architecture and related 7.2 split [5]. In detail, a UCCF M-MIMO network (or more specifically its part) based on open RAN architecture is constituted by a single virtualized Central Unit (CU), virtualized Distributed Unit (DU), and multiple physical Radio Units (RUs). The virtualized CU-DU coordinates the RUs and is jointly responsible for user scheduling and high physical layer functions, e.g., calculation of precoding weights and modulation and coding scheme (MCS) selection. The physical RUs distributed over the cell area perform low physical layer functions and Radio Frequency (RF) processing of the signal, e.g., modulation, FFT, and application of precoder weights. However, the canonical implementation of UCCF M-MIMO assumes that every user is being served by all RUs. This approach would require enormous computational resources, e.g., to estimate the radio channel coefficients between all RUs and all UEs, compute large matrices of precoding weights, and perform resource scheduling. As in practice, both the virtualized CU-DU and each of the physical RUs have limited computational resources, it is of high importance to ensure reasonable scaling of a UCCF M-MIMO network. Moreover, some RUs that are placed far away from the UE would provide minor performance benefits compared to the required signaling overhead and demand for computational resources. From this perspective, it is of high importance to limit the number of RUs that are serving a single UE to balance performance gains and the growing computational and signaling overhead. In UCCF M-MIMO, this is achieved through the formulation of so-called serving clusters [6]. Serving clusters are dynamically formulated by the centralized CU-DU using various algorithms, e.g., those based on RSS or advanced Machine Learning (ML) solutions, which are aimed at meeting different optimization goals, like load balancing, energy efficiency, or throughput maximization. The architecture of UCCF M-MIMO is summarized in Figure 1.  
The user-centric cell-free M-MIMO network is an emerging topic regarding 6G, and numerous works focusing on simulation studies of such systems have already been published. However, most of them, even those coauthored by highly cited experts in the M-MIMO field like [4; 7; 8; 9; 10], utilize relatively simple system models. In most cases, these models involve single-carrier, narrowband transmission.