Abstract
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).
Highlights
Among all the proposed AP switch ON/OFF (ASO) strategies, the most adequate to be implemented in a cell-free massive multiple-input multiple-output (MIMO) scenario, based on the use of very large-scale network-state information, would be the spatial regularity-based greedy ASO (SR-ASO), as it is the one providing the best performance versus complexity/implementability trade-off
The proposed framework considers the use of different ASO strategies designed to dynamically turn ON/OFF some of the access points (APs) based on the number of active mobile stations (MSs) in the network
Six ASO strategies have been proposed: the pure random selection scheme, denoted as random selection ASO (RS-ASO), three selection strategies aiming at keeping the locations of the set of active APs as uniform as possible, denoted as mixture discrepancy-based greedy ASO (MD-ASO), nearest neighbour-based ASO (NN-ASO) and SR-ASO, a selection strategy that exploits the availability of time-dynamic information about short-term traffic variations, denoted as propagation losses-aware ASO (PL-ASO) and, a greedy optimal selection strategy, denoted as OG-ASO
Summary
Related to our work in this paper, Zhang et al [15], Alonzo et al [16], and Bashar et al [17], following the way paved by Ngo et al [4], analyze and optimize the energy efficiency of cell-free massive MIMO networks under different scenarios, including the presence of hardware impairments, the use of millimeter-wave frequency bands or the use of capacity-constrained fronthaul links during the uplink (UL) payload transmission phase. The proposed framework is a non-trivial generalization of previous mathematical models for cell-free massive MIMO networks in [2]–[4], [13], which considered exclusively the propagation through NLOS channels, and in [26], which contemplated the presence of a LOS but limiting the study to single-antenna APs, neglecting spatial correlation effects. CN (m, R) denotes a circularly symmetric complex Gaussian vector distributions with mean m and covariance R, N (0, σ 2) denotes a real valued zero-mean Gaussian random variable with standard deviation σ , and U[a, b] represents a random variable uniformly distributed in the range [a, b]
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