Network densification, massive multiple-input multiple-output (MIMO) and millimeter-wave (mmWave) bands have recently emerged as some of the physical layer enablers for the future generations of wireless communication networks (5G and beyond). Grounded on prior work on sub-6 GHz cell-free massive MIMO architectures, a novel framework for cell-free mmWave massive MIMO systems is introduced that considers the use of low-complexity hybrid precoders/decoders while factors in the impact of using capacity-constrained fronthaul links. A suboptimal pilot allocation strategy is proposed that is grounded on the idea of clustering by dissimilarity. Furthermore, based on mathematically tractable expressions for the per-user achievable rates and the fronthaul capacity consumption, maxmin power allocation and fronthaul quantization optimization algorithms are proposed that, combining the use of block coordinate descent methods with sequential linear optimization programs, ensure a uniformly good quality of service over the whole coverage area of the network. Simulation results show that the proposed pilot allocation strategy eludes the computational burden of the optimal small-scale CSI-based scheme while clearly outperforming the classical random pilot allocation approaches. Moreover, they also reveal the various existing trade-offs among the achievable max-min per-user rate, the fronthaul requirements and the optimal hardware complexity (i.e., number of antennas, number of RF chains).

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This paper introduces a novel technique, clustered cell-free massive-MIMO (C 2 F-M-MIMO), that generalizes the recently proposed cell-free massive MIMO (CF-M-MIMO) concept by optimizing the connectivity pattern among access points (APs) and mobile stations (MSs). Relying on the popular Kmeans clustering algorithm, APs and MSs are grouped together in clusters in such a way that strong interferers arising due to pilot contamination are minimized. The clustering pattern varies in accordance to the large-scale fading parameters and is therefore able to respond to macroscopic changes in the network (user mobility, network load variations). Numerical results show that the proposed architecture is able to support a large number of users even with very low-complexity processing at the AP side while greatly reducing the fronthaul capacity requirements.

In cell-free massive MIMO networks, a large number of distributed access points (APs) provide service to a much smaller number of mobile stations (MSs) over the same time/frequency resources. The key idea is to use a central processing unit (CPU) to manage such a densely populated network of APs. This centralization helps reducing operational costs and eases implementation of joint power control and coherent signal processing through a proper orchestration of the functional split between the CPU and the APs. Cell-free massive MIMO networks, however, are often subject to stringent capacity requirements on the fronthaul links connecting the APs to the CPU and thus, low-resolution ADCs must be used to quantize the signals shared among CPU and APs. In this paper, analytical closed-form expressions for the achievable user rates on both the uplink (UL) and downlink (DL) of a fronthaul-capacity constrained cell-free massive MIMO network using low-resolution ADCs are obtained. These expressions, jointly with the use of theoretical models characterizing the fronthaul capacity consumption of different CPU-AP functional splits, allow posing max-min fairness power control optimization problems that can be solved using standard convex optimization algorithms. Numerical results show that, under fronthaul capacity constraints, CPU-AP functional splits where the precoding/decoding schemes are implemented at the APs are clearly outperformed by those functional splits in which, thanks to sharing CSI among APs and CPU, the precoding/decoding functions are implemented at the CPU. In contrast, if the limiting factor is the resolution of the ADCs used to quantize the samples to be transmitted on the fronthaul links, the preferred CPU-AP functional splits are those in which the baseband processing is performed at the APs. Moreover, they also reveal that in such functional splits there is always an optimal range of values of the UL fronthaul capacity fraction allocated to share the CSI. INDEX TERMS Cell-free massive MIMO, capacity-constrained fronthaul, normalized conjugate beamforming, matched filtering, CPU-AP functional split, low-resolution ADCs.

The combination of user-centric network densification and distributed massive multiple-input multiple-output (MIMO) operation has recently brought along a new paradigm in the wireless communications arena, referred to as cell-free massive MIMO networking. In these networks, a large number of distributed access points (APs), coordinated by a central processing unit (CPU), cooperate to coherently serve a large number of mobile stations (MSs) in the same time/frequency resource. Similar to what has been traditionally done with conventional cellular networks, cell-free massive MIMO networks will be dimensioned to provide the required quality of service (QoS) to MSs under heavy traffic load conditions, and thus they might be underutilized during low traffic load periods, leading to an inefficient use of both spectral and energy resources. Aiming at the implementation of green cell-free massive MIMO networks, this paper proposes and analyzes the performance of different AP switch ON/OFF (ASO) strategies designed to dynamically turn ON/OFF some of the APs based on the number and/or location of the active MSs in the network. The proposed framework considers line-of-sight (LOS) and non-line-of-sight (NLOS) links between APs and MSs, the use of different antenna array architectures at the access points (APs), suitably characterized by array-dependent spatial correlation matrices, and specific power consumption models for APs, MSs and fronthaul links between the APs and the CPU. Numerical results show that the use of properly designed ASO strategies in cell-free massive MIMO networks clearly improve the achievable energy efficiency. Moreover, they also reveal the existing trade-offs among the achievable energy efficiency, the available network-state information, and the hardware configuration (i.e., number of APs, number of transmit antennas per AP, and number of MSs). INDEX TERMS AP ON/OFF switching, green networking, cell-free massive MIMO, zero-forcing precoding.

Cell-free massive multiple-input multiple-output (MIMO) is a novel beyond 5G (B5G) and 6G paradigm that, through the use of a common central processing unit (CPU), coordinates a large number of distributed access points (APs) to coherently serve mobile stations (MSs) on the same time/frequency resource. By exploiting the characteristics of new less-congested millimeter wave (mmWave) frequency bands, these networks can improve the overall system spectral and energy efficiencies by using lowcomplexity hybrid precoders/decoders. For this purpose, the system must be correctly dimensioned to provide the required quality of service (QoS) to MSs under different traffic load conditions. However, only heavy traffic load conditions are usually taken into account when analysing these networks and, thus, many APs might be underutilized during low traffic load periods, leading to an inefficient use of resources and waste of energy. Aiming at the implementation of energy-efficient AP switch on/off strategies, several approaches have been proposed in the literature that only consider rather unrealistic uniform spatial traffic distribution in the whole coverage area. Unlike prior works, this paper proposes energy efficient AP sleep-mode techniques for cell-free mmWave massive MIMO networks that are able to capture the inhomogeneous nature of spatial traffic distribution in realistic wireless networks. The proposed framework considers, analyzes and compares different AP switch ON-OFF (ASO) strategies that, based on the use of goodness-of-fit (GoF) tests, are specifically designed to dynamically turn on/off APs to adapt to both the number and the statistical distribution of MSs in the network. Numerical results show that the use of properly designed GoF-based ASO strategies under a non-uniform spatial traffic distribution can serve to considerably improve the achievable energy efficiency.INDEX TERMS Cell-free massive MIMO, energy efficiency, access-point switch on/off techniques, millimeter-wave communications, goodness-of-fit.

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