随着第六代(6th generation, 6G)移动通信系统的研究与发展，无蜂窝大规模多输入多输出(Cell Free massive Multiple Input Multiple Output, CF-mMIMO)系统作为一项关键的候选技术受到了广泛关注。CF-mMIMO系统能够解决传统蜂窝网络所固有的边界效应，从而为服务区内的用户提供相对均匀的服务质量。速率分割多址(Rate Splitting Multiple Access, RSMA)作为未来无线通信系统的关键技术之一，能够灵活地管理干扰与噪声，且具有较好的用户公平性与海量连接性。二者的有机结合(RSMA-CF-mMIMO)有望在降低干扰的同时保证大规模接入，从而实现较高的数据速率与系统吞吐量。此外，接入点(Access Point, AP)与中央处理器之间容量有限的前传链路，以及AP处的硬件质量都是影响系统性能的重要因素。基于此，本文研究了上行RSMA-CF-mMIMO系统在有限前传容量下的系统性能。本论文的主要工作如下：

1. 研究了上行RSMA-CF-mMIMO系统在瑞利信道下的系统性能。考虑不完全的信道状态信息，以及有限前传容量的限制，基于率失真理论，对前传噪声进行了建模与推导。采用最大比合并方案，分别推导了采用和未采用大尺度衰落译码时，用户可达速率的理论表达式。通过仿真结果验证了理论分析的准确性，并分析了用户数量、导频长度、功率分配因子对系统性能的影响。

2. 研究了上行RSMA-CF-mMIMO系统在空间相关莱斯信道下的系统性能。采用局部散射模型引入了AP处多天线的空间相关性，并给出了莱斯信道下的MMSE信道估计。随后经过理论推导，给出了用户的可达速率，通过仿真评估了AP数量、空间相关性和前传容量等因素对系统性能的影响。结果表明，空间相关性将会为系统带来性能损失，而莱斯信道由于视距分量的存在，其性能通常要优于瑞利信道的对应情形。

3. 研究了低精度模数转换器(Analog to Digital Converter, ADC)下，上行RSMA-CF-mMIMO系统的性能。首先，通过加性量化噪声模型来描述低精度ADC对导频符号与数据符号的影响。推导了存在低精度ADC量化的MMSE信道估计，随后对经历过低精度ADC量化的数据符号进行最大比合并，从而推导出用户的可达速率。仿真结果表明，低精度ADC会降低信道估计质量，并带来非零的估计误差下限。此外，由于低精度ADC的影响，空间相关性对系统性能的影响将变弱。

With the research and development of the 6th generation (6G) mobile communication system, Cell-Free massive Multiple Input Multiple Output (CF-mMIMO) systems have garnered widespread attention as a key candidate technology. CF-mMIMO systems can address the inherent boundary effects of traditional cellular networks, thereby providing relatively uniform service quality for users within the coverage area. Rate Splitting Multiple Access (RSMA), as one of the key technologies for future wireless communication systems, can flexibly manage interference and noise while offering better user fairness and massive connectivity. The organic combination of these two technologies (RSMA-CF-mMIMO) holds the potential to reduce interference while ensuring large-scale access, thus achieving higher data rates and system throughput. Furthermore, the limited capacity of the fronthaul link between the access point (AP) and the central processing unit, as well as the hardware quality at the AP, are significant factors affecting system performance. Based on the above, this thesis investigates the system performance of the uplink RSMA-CF-mMIMO system under limited fronthaul capacity. The main work of this thesis is as follows:

The system performance of the uplink RSMA-CF-mMIMO system under Rayleigh fading channels is studied. Considering imperfect channel state information (CSI) and fronthaul capacity constraints, a rate-distortion theoretic approach is employed to model and derive the fronthaul noise. By adopting the maximum ratio combining (MRC) scheme, theoretical expressions for the achievable user rate are derived for both cases: with and without large-scale fading decoding. Simulation results validate the accuracy of the theoretical analysis and further investigate the impact of key system parameters, including the number of users, pilot sequence length, and power allocation factor, on system performance.

The performance of the uplink RSMA-CF-mMIMO system under spatially correlated Rician fading channels is studied. A local scattering model is introduced to characterize the spatial correlation among multiple antennas at the APs, and the minimum mean square error (MMSE) channel estimation under Rician fading is derived. Based on the theoretical analysis, the achievable user rate expressions are obtained. Simulations are conducted to evaluate the impact of key factors such as the number of APs, spatial correlation, and fronthaul capacity. Results demonstrate that spatial correlation deteriorates system performance, while the presence of a line-of-sight (LoS) component in Rician fading generally leads to better performance compared to the Rayleigh fading case.

The performance of the uplink RSMA-CF-mMIMO system employing low-resolution Analog-to-Digital Converters (ADCs) is investigated. First, the effect of low-resolution ADCs on both pilot and data symbols is characterized using an additive quantization noise model. The MMSE channel estimation under quantization effects is derived, followed by the application of MRC to the quantized data symbols to obtain the achievable user rate. Simulation results reveal that low-resolution ADCs degrade channel estimation quality and introduce a non-zero lower bound on estimation errors. Moreover, the impact of spatial correlation on system performance diminishes due to the presence of low-resolution ADCs.

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