## 1 Introduction
The introduction of wider SCS is essential for mitigating inter-carrier interference (ICI) and phase noise at millimeter-wave (mmWave) frequencies. As SCS widens, the TTI assumes smaller values ranging from 1 ms to 31.25 \(\upmu\)s. 5G NR was designed to lower interference and increase EE by reducing always-on transmissions, which is a crucial aspect to extend the lifetime of IoT devices. 5G NR ensures forward compatibility as it is prepared for its future 6G evolution in use cases and technologies. The introduction of mini-slots made 5G NR capable of guaranteeing low-latency requirements for ultrareliable and low-latency communication (URLLC). At mmWave frequencies, high capacity and extreme data rates are possible, even though higher frequencies introduce limitations in coverage due to increased signal attenuation [31]. 5G NR specifications are projected to accommodate dense urban scenarios, but with the introduction of mmWave, it can be more likely to have some part of the signal blocked by obstacles or severely affected by distance, which causes a substantial decrease in the signal strength, becoming hard to compensate even with advanced signal processing techniques [32; 33]. Motivated by the work above, in this paper we study the adoption of precoding-aided transmitter-side generalized space-frequency IM (PT-GSFIM) as an alternative multiuser MIMO (MU-MIMO) scheme for the downlink in 5G and beyond systems. Besides exploiting both frequency and space domain resources through IM, different \(M\)-QAM constellation orders can be supported so as to accommodate flexible tradeoffs between SE, performance and complexity. In this paper we extend our previous work in [25], where we presented the design of a precoder and different detection algorithms for PT-GSFIM. The present scheme uses the same precoder to remove the MUI between BS and receivers, breaking the MU communication into equivalent small SU links and achieving a lower complexity at the receiver side. Furthermore, we adopt subcarrier-level interleaving combined with SSDtechniques as it allows the proposed approach to exploit the inherent diversity in frequency-selective channels and achieve improved performance without requiring additional power or bandwidth. In this paper, we focus on the system-level assessment of a C-RAN based on the PT-GSFIM scheme. The system-level evaluation is performed for numerologies 1 and 2 from 5G NR in three 3D scenarios with different parameters, such as modulation, number of transmitted antennas per user, bandwidth and frequency carrier, and it is benchmarked against two alternative MU-MIMO schemes. System-level simulations were performed based on PT-GSFIM link-level results between the BSs and the multiple terminals, where it is considered that the alternating direction method of multipliers (ADMM) detection algorithm from [25] is applied on the receiver side. System-level evaluation demonstrates that C-RAN deployments in several indoor and outdoor scenarios, including urban or mixed, can achieve significant performance and coverage improvements over typical cellular networks. The paper is organized as follows: Section 2 presents the model for the PT-GSFIM system. Section 3 presents the transmitter and receiver structure as well as the different 5G NR scenarios that are considered in the evaluation. Section 4 presents and discusses the link- and system-level simulation results. The conclusions are outlined in Section 5. Notation: Matrices and vectors are denoted by uppercase and lowercase boldface letters, respectively. (. )\({}^{\rm T}\) and (. )\({}^{\rm H}\) denote the transpose and conjugate transpose of a matrix/vector, \(\parallel\,\cdot\,\parallel_{p}\) is the \(\updownarrow_{p}\)-norm of a vector, \(\parallel\,\cdot\,\parallel_{0}\) is its cardinality, \(\rm{supp}(\mathbf{x})\) returns the support of \(\mathbf{x}\), \(\left\lfloor\cdot\right\rfloor\) is the floor function and \(\mathbf{I}_{n}\) is the \(n\times n\) identity matrix.