# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction
### Motivation  
To configure a user-centric cell-free massive MIMO system, each UE should select an AP subset that can ensure good performance while pilot contamination is minimized. However, the problem of ensuring the connection of all users within the coverage area while simultaneously limiting the number of UEs serviced by an AP is NP-hard and has very high computational complexity [10]. Therefore, a heuristic approach is more suitable [10]. The AP-UE association process can take various forms. These include each AP individually selecting the UEs that it directly supports. Each UE selects the AP subset that supports it. Additionally, there is a combined approach that incorporates both methods. In [21],the association process involves each AP comparing the large-scale fading coefficients (LSPCs) of all UEs it serves. Each AP calculates the LSFC for all UEs and then supports them in order of highest LSFC. To avoid strong pilot contamination, the number of users that can be supported is limited. However, because the association is formed by the AP, in situations with a large number of UEs, UEs with relatively low LSFC values may risk losing connectivity. In particular, in environments with a high number of UEs, if the number of UEs that an AP can support is fixed, such unfair access will become more prevalent. Therefore, there is a need to provide a method that can guarantee connectivity for all UEs. In [12], each AP is limited to supporting a number of UEs equal to the length of the orthogonal pilot. Initially, each UE selects the AP with the largest LSFC as its master AP. Then, for each pilot, each AP supports the UE with the best channel condition. This approach proposes a scalable AP-UE association by ensuring connectivity for all UEs while the number of UEs connected to each AP is limited. However, this method assumes that if the number of UEs per AP exceeds the number of pilots, multiple UEs can be assigned to the same pilot but are multiplexed in time and/or frequency resources to avoid pilot contamination, as described in [12]. While this approach can mitigate pilot contamination, it relies on multiple access techniques that allocate different time or frequency resources to each UE. In [22], the process involves sorting UEs based on LSFC for each AP. Then, it sequentially connects a number of UEs equal to the length of the orthogonal pilot and prioritizes those with higher LSFC. This process repeats until all APs are fully connected. In this process, unconnected UEs will request a connection to the AP with the highest LSFC. While this allows all UEs to be connected, in environments with a large number of UEs, there will likely be an increase in overserving APs that suffer from significant pilot contamination. This will prevent the system from guaranteeing good performance. In [23], it is assumed that each UE knows the LSFC with all APs. The clustering is then conducted by connecting to APs with LSFC values exceeding a certain threshold. Similar to [22], this approach ensures connectivity for all UEs, but this leads to an imbalance in the load among APs. Additionally, the above studies do not consider methods to reduce fronthaul capacity as the number of users increases in massive access scenarios. In practical cell-free massive MIMO systems, the fronthaul capacity is not infinite. Additionally, the large number of APs in these systems leads to increased power consumption when high-resolution ADCs are used. On the other hand, low-resolution ADCs can mitigate this issue by reducing power consumption and hardware costs, as well as decreasing the fronthaul load between the APs and the CPU. Consequently, research works on the application of low-resolution ADCs in cellular and cell-free massive MIMO systems are actively being conducted. In [24], the use of low-resolution ADCs in wireless communication systems is surveyed, emphasizing their benefits in reducing power consumption and hardware complexity.