Algorithm

Engineering

Coordinate descent

MIMO

Quantization (signal processing)

Telecommunications

Computer science

Distributed computing

Beamforming

Computer network

Electronic engineering

Cluster analysis

Channel (broadcasting)

Extremely high frequency

Base station

Optimization problem

Machine learning

IEEE Access

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, max-min 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. The 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., the number of antennas and the number of RF chains).

Cell-Free Millimeter-Wave Massive MIMO Systems With Limited Fronthaul Capacity