User-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO), commonly called cell-free mMIMO, is an essential technology for ensuring more uniform coverage and higher spectral and energy efficiencies in next-generation communication systems. This paper investigates an alternative effective channel estimation method to address the issue of the lower channel hardening degree experienced by UC D-mMIMO systems. Specifically, this paper proposes a hybrid approach for effective channel estimation, allowing users to estimate their channels either through downlink pilot-based training, by using a blind algorithm, or by relying on statistical channel state information (CSI). The hybrid estimation method is compared with the case where each method is applied individually, as well as with the ideal case of perfect CSI. The analysis is conducted using various system parameter settings, such as the number of antennas and users, and it accounts for the presence of pilot contamination. Simulation results reveal that the proposed hybrid channel estimation algorithm is able to provide the best spectral efficiency performance in any network parameter configuration, while reducing the estimation normalized mean-square error compared to conventional statistical CSI.

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems with multiple central processing units (CPUs), commonly called cell-free mMIMO. Specifically, a framework incorporating processing capacity and inter-CPU communication constraints is proposed. Two methods are presented for limiting the number of radio units (RUs) serving each user equipment (UE). The first method is performed by the CPUs, while the second one is implemented at the UEs and RUs. Both methods prevent the computational complexity (CC) for channel estimation and precoding signals from increasing with the number of RUs. The backhaul signaling demands are presented and modeled, and it is considered that each CPU can serve only a restricted number of UEs managed by other CPUs to mitigate inter-CPU communication. Two strategies to adjust the RU clusters according to the network implementations are also proposed. We compare the proposed approaches with a traditional scalable UC system. Simulation results reveal that the proposed techniques allow UC systems to keep their spectral efficiency (SE) under minor degradation while reducing the CC by 98% and improving energy efficiency (EE). Besides, managing inter-CPU communication controls backhaul traffic effectively, and RU cluster adjustments further reduce CC.

This paper investigates the performance of scalable user-centric (UC) distributed massive multiple-input multiple-output (D-mMIMO) systems, widely known in the literature as cell-free mMIMO, with limited processing capacity. Specifically, it is assumed that the computational complexity (CC) of performing channel estimation and precoding signals does not increase with the number of access points (APs). In this regard, it is considered that each user equipment (UE) can only be associated with a finite number of APs. Moreover, a method is proposed for adjusting the AP clusters according to the network implementation, i.e., centralized or distributed. We compare the proposed approaches with a scalable UC system that does not perform AP cluster adjustment and does not prevent the processing demands from growing with the number of APs. Simulation results reveal that UC systems can keep the spectral efficiency (SE) under minor degradation even if the processing capacity is limited, reducing the CC by up to 96%. Besides, the proposed method for adjusting the AP cluster leads to further reductions in CC.

This paper investigates the performance of downlink (DL) pilot-based training to estimate the effective channel in user-centric cell-free massive multiple-input multiple-output (MIMO) networks. An algorithm for DL pilot assignment is proposed based on the level of interference between each user equipment (UE). It is proposed a refinement method for access point (AP) selection that controls the maximum AP cluster size of UEs. The strategy aims to control the maximum number of APs serving each UE to reduce the disparities among the AP cluster sizes. DL pilot-based training is compared with the blind, perfect and statistical channel state information (CSI) methods, assuming different precoding techniques, AP selection schemes, and the presence of pilot contamination. Our results demonstrate the following: (i) the proposed DL pilot assignment algorithm outperforms the baseline solutions; (ii) the proposed AP selection refinement method can improve the energy efficiency up to 86.6% without compromising the spectral efficiency; and (iii) DL pilot-based estimation reduces the normalized mean-square error significantly compared with blind and statistical CSI methods.

The 5G mobile communication systems introduce multiples functional splits between base station elements, new transmission bands and a large number of antennas supporting beamforming. In this scenario, a viable strategy to avoid building penetration losses is to deploy the antenna elements indoor and use a fronthaul link to establish the connection between them and the rest of the radio access network. This work explores a 5G fronthaul scheme based on analog radio over coaxial cables, leveraging this existing fixed access infrastructure to facilitate 5G deployments. Results indicate that the fronthaul solution discussed here is able to support the high capacity requirement of 5G, with the additional benefits of low transmit power, low latency and low cost. The main novelty of this paper is to investigate the potential of this kind of fronthaul architecture considering both the physical layer and economic aspects.