# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction
### Contribution  
As mentioned in the above research works, in user-centric cell-free massive MIMO systems, AP-UE association can lead to load imbalance among APs or result in low-LSFC UEs not being connected. Even when load balancing is implemented by limiting the number of UEs connected to each AP to ensure connectivity for all UEs, there is still a lack of consideration for reducing the required fronthaul capacity in environments with a large number of UEs. Therefore, in this study, a heuristic-based AP-UE association method is proposed for user-centric cell-free massive MIMO systems using low-resolution ADCs. The association utilizes LSFC between APs and UEs. It takes into account imperfect channel state information (CSI). Additionally, this paper assumes that APs are equipped with low-resolution ADCs to reduce fronthaul capacity in massive access scenarios while considering practical conditions. Research on cellular and cell-free massive MIMO systems using low-resolution ADCs is actively underway because low-resolution ADCs can reduce the exponentially increasing power consumption and costs associated with resolution. Additionally, it can also reduce the fronthaul load between APs and CPUs [15; 16; 17; 25; 26; 27]. The main contributions of this study are as follows:  
* Since we assume a user-centric cell-free massive MIMO environment with limited fronthaul capacity and a very large number of UEs, we consider the deployment of APs equipped with low-resolution ADCs. The quantization noise model due to low-resolution ADCs is described as the additive quantization noise model (AQNM), as depicted in [26]. Additionally, we derive closed-form expressions for uplink SE in systems using low-resolution ADCs.
* Although there is existing research on user-centric cell-free massive MIMO, there are few studies that conduct simulations in massive access scenarios where the number of UEs exceeds the number of APs. Some studies propose association algorithms that can accommodate a large number of UEs, but in environments with a very high number of UEs, these approaches often result in overserving APs or fail to guarantee UE connectivity. Overserving APs, which lead to pilot contamination, significantly degrade SE performance. In response, we propose an AP-UE association algorithm that prevents overserving APs and ensures the connectivity of all UEs in massive access scenarios.
* This study specifically focuses on scenarios where UEs share the same time-frequency resources, which is critical for understanding the impact of pilot contamination in dense network environments. Unlike traditional approaches that rely on multiple access techniques, our method manages pilot contamination and maintains connectivity and performance within the constraints of shared resources.
* In a user-centric cell-free massive MIMO system, the association problem, which guarantees connectivity for all UEs while the number of UEs supported by each AP is limited, is NP-hard and computationally intensive. Therefore, in this study, we propose an association algorithm using a heuristic approach to address this challenge.
* The proposed approach not only enhances connectivity and performance in current networks but also lays the groundwork for scalable solutions in future large-scale Internet-of-Things (IoT) and 6G deployments, where efficient resource management and reduced fronthaul load are critical.