# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 5 Simulation Results
### Performance Analysis
Conversely, the proposed algorithm maintains its effectiveness because it does not have overserving APs or unconnected UEs, thus confirming its robustness in handling massive access scenarios. Figure 4 compares the average SE per UE performance based on the number of users and quantization bits. Specifically, when evaluating the 3-bit to 5-bit quantization interval, the proposed algorithm experiences a performance reduction of approximately 53% for the LP-MMSE combiner and about 36% for the MR combiner as the number of UEs increases from \(K=100\) to \(K=160\). In contrast, the structured algorithm, which suffers from unconnected UEs, shows a performance drop of approximately 93% within the same quantization range. The scalable algorithm, which encounters fewer overserving APs, exhibits a 95% decline, while the grant algorithm, with a higher occurrence of overserving APs, sees a 97% decrease. This analysis highlights that the proposed algorithm maintains superior performance under increased UE load, effectively managing overserving APs and unconnected UEs, unlike other algorithms that experience significant SE degradation due to pilot contamination and resource allocation inefficiencies. Figure 3: Spectral efficiency performance results based on the number of users and combining vectors. When quantizing with 3 bits and 5 bits, the average SE performance per UE can be compared. With 100 UEs, the LP-MMSE combiner shows 20% better performance with 5 bits compared to 3 bits, while the MR combiner shows a 10% improvement. With 160 UEs, the LP-MMSE combiner performs 10% better with 5 bits compared to 3 bits, and the MR combiner shows a 5% improvement. This shows that as the number of UEs increases, the gap in average SE performance between 3-bit and 5-bit quantization decreases. As the quantization bit increases, an improvement in the average SE performance per UE is observed. However, this also leads to an increase in the required fronthaul capacity and power consumption. Figure 4(a) shows the total fronthaul capacity required based on the quantization bit and the number of UEs, calculated using Equation (56) from [26]. When the number of UEs increases from 100 to 160 with 3-bit quantization, the required fronthaul capacity increases by 60%. Additionally, when comparing 3-bit to 5-bit quantization with 100 UEs, the required fronthaul capacity increases by approximately 66%. On the other hand, when using 5-bit quantization with 100 UEs, the required fronthaul capacity is about 4% higher than with 3-bit quantization and 160 UEs. This shows that 3-bit quantization allows for accommodating more UEs than 5-bit quantization. Furthermore, with reduced fronthaul capacity requirements, the CPU's fronthaul load also decreases compared to 5-bit  
Figure 4: Average SE based on the number of users, combining techniques, and quantization bit. quantization. Therefore, in massive access scenarios with a large number of UEs, reducing the quantization bits can decrease the fronthaul load while allowing the network to support many UEs. Figure 4(b) shows the total power consumption based on the quantization bit, calculated using Equation (28) from [27]. When the number of UEs is 100, 5-bit quantization consumes about 44% more power than 3-bit quantization. Additionally, when the number of UEs is 160, 5-bit quantization consumes about 26% more power than 3-bit quantization. Comparing this to the average SE performance, with 100 UEs, 5-bit quantization shows a 20% increase in average SE performance over 3-bit quantization, but the power consumption increases by 44%. With 160 UEs, the average SE performance increases by 10%, but the power consumption increases by 26%. This indicates that in environments with a large number of UEs, the difference in average SE performance between 3-bit and 5-bit quantization is not significant, making 3-bit quantization more effective in reducing power consumption.