# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 1 Introduction  
Massive multiple-input-multiple-output (MIMO) is a key technology in fifth-generation (5G) wireless communication systems that support a large number of users simultaneously using a significant number of antennas at an access point (AP) or base station (BS) with the same resources [1]. The channel hardening and favorable propagation characteristics of massive MIMO can increase spectral efficiency (SE) and energy efficiency (EE). With simple linear signal processing techniques at the AP, it can achieve nearly optimal performance [2]. Additionally, massive MIMO has been demonstrated as a promising technology to support massive access. It achieves this by leveraging spatial degrees of freedom, enabling simultaneous transmission by multiple users, as demonstrated in [3]. The cellular structure based on this massive MIMO has been the subject of many studies. However, users located at the cell's edge experience reduced performance due to their distance from the AP and interference from other cells in the vicinity, leading to performance degradation. There is also an issue of significant performance differences for users located at the center of the cell [4]. Cell-free massive MIMO, unlike traditional cellular systems, involves multiple APs within the coverage area collaborating to support users. This approach promisesbetter performance and is being researched as a key technology for beyond fifth-generation (B5G) and sixth-generation (6G) networks, with the potential to improve the edge effect observed in traditional cellular networks. In particular, cell-free massive MIMO networks can provide wider coverage and are resistant to shadow fading, making them suitable for massive access scenarios [5]. In traditional cell-free massive MIMO, multiple APs and user equipment (UEs) are distributed within the service area, and all UEs communicate with the APs using the same time-frequency resources [6]. All APs are connected to the central processing unit (CPU) through a fronthaul link. This connection allows them to share information. The CPU performs encoding or decoding of the data received from the APs [7]. Traditional cell-free massive MIMO has the advantage of achieving higher SE, EE, and coverage probability compared to conventional cellular systems [8]. However, in traditional cell-free massive MIMO systems, where all APs simultaneously support all UEs, the fronthaul capacity requirements and computational complexity increase significantly as the number of APs and UEs in the network grows [9]. User-centric cell-free massive MIMO, which is an alternative structure to cell-free massive MIMO, involves subsets of APs forming clusters to support UEs. This approach can reduce the load on the fronthaul links compared to traditional cell-free massive MIMO and achieve scalability [10; 11; 12]. Even in massive access scenarios with a very large number of UEs, user-centric cell-free massive MIMO has the advantage of guaranteeing better performance compared to traditional cellular systems. Additionally, since user-centric cell-free massive MIMO can reduce the fronthaul load, it is more suitable for massive access scenarios than traditional cell-free massive MIMO. However, due to the large number of UEs, some UEs may not be supported by any AP. Also, in user-centric cell-free massive MIMO, channel estimation is performed in the uplink phase using orthogonal pilots among UEs. While massive access scenarios can involve various methods for resource allocation, this study focuses on scenarios where UEs share the same time-frequency resources. This means they utilize the same frequency bands and time slots for communication. Consequently, the limited number of orthogonal pilots becomes a significant issue, as the orthogonal pilots become insufficient to uniquely identify each UE, leading to strong pilot contamination. This contamination occurs when multiple UEs are assigned the same pilot sequence, causing interference during channel estimation.