# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 6 Conclusions  
In this paper, we propose an AP-UE association algorithm for massive access in a user-centric cell-free massive MIMO system employing low-resolution ADCs. Current user-centric cell-free massive MIMO systems typically assume infinite fronthaul capacity and the use of high-resolution ADCs by APs. However, in reality, fronthaul capacity is not infinite. Additionally, in massive access scenarios, there is a need for methods to reduce the required fronthaul capacity. Hence, this study introduces the adoption of low-resolution ADCs and approximates the quantization noise associated with these ADCs using the AQNM model. Additionally, we derive a closed-form expression for the achievable rate. Furthermore, we propose an AP-UE association algorithm based on heuristic approaches that ensure performance even in massive access scenarios. The existing association methods do not consider the scenario of massive access in which the number of UEs far exceeds the number of APs, and they perform association only from the perspective of APs or UEs. However, the proposed association method considers the scenario of massive access in the user-centric cell-free massive MIMO system with low-resolution ADCs. We compare its performance with existing methods in terms of SE per UE. Through performance comparison in terms of SE per UE, the proposed algorithm shows better performance than existing algorithms. Moreover, at \(K=160\), while existing algorithms fail to guarantee performance, the proposed algorithm ensures the performance. Figure 5: Total required fronthaul capacity and total power consumption based on the quantization bit. Additionally, we performed simulations to require fronthaul capacity and power consumption when utilizing low-resolution ADCs. Our findings indicate that reducing quantization bits in massive access scenarios reduces fronthaul load and enhances the capability to accommodate more UEs. Furthermore, as the number of UEs increases, the performance gap in average SE between 3-bit and 5-bit quantization diminishes. Specifically, in terms of power efficiency, our simulations confirmed that 3-bit quantization outperforms 5-bit quantization in massive access scenarios. Future research needs to more accurately predict the quantization model that exhibits nonlinear characteristics due to low-resolution ADCs. While the AQNM model provides a useful framework for approximating quantization noise, it has limitations, particularly in accurately capturing the nonlinear characteristics introduced by low-resolution ADCs. The model assumes a linear approximation which may not fully account for the complexity of signal distortions in practical scenarios. Future research needs to develop more accurate quantization models that can predict these nonlinear effects with greater precision. Additionally, accurate channel estimation will also be necessary. Deep learning could enable more precise predictions of these nonlinear characteristics and also improve the accuracy of channel estimation. This would significantly impact performance improvement. Conceptualization, [PERSON] and [PERSON]; methodology, [PERSON] and [PERSON]; validation, [PERSON] and [PERSON]; investigation, [PERSON] and [PERSON]; resources, [PERSON] and [PERSON]; data curation, [PERSON]-[PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]-H.Y. and H.-K.S. ; funding acquisition, H.-K.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the Technology Innovation Program (RS-2022-00154678, Development of Intelligent Sensor Platform Technology for Connected Sensor) funded By the Ministry of Trade, Industry & Energy (MOTIE, Korea).