2 research outputs found
Clustering Based Hybrid Precoding Design for Multi-User Massive MIMO Systems
Hybrid precoding has been recognized as a promising technology to combat the
path loss of millimeter wave signals in massive multiple-input multiple-output
(MIMO) systems. However, due to the joint optimization of the digital and
analog precoding matrices as well as extra constraints for the analog part, the
hybrid precoding design is still a tough issue in current research. In this
paper, we adopt the thought of clustering in unsupervised learning and provide
design schemes for fully-connected hybrid precoding (FHP) and
adaptively-connected hybrid precoding (AHP) in multi-user massive MIMO systems.
For FHP, we propose the hierarchical-agglomerative-clustering-based (HAC-based)
scheme to explore the relevance among RF chains in optimal hybrid procoding
design. The similar RF chains are merged into an individual RF chain when
insufficient RF chains are equipped at the base station (BS). For AHP, we
propose the modified-K-means-based (MKM-based) scheme to explore the relevance
among antennas at the BS. The similar antennas are supported by the same RF
chain to make full use of the flexible connection in AHP. Particularly, in
proposed MKM-based AHP design, the clustering centers are updated by
alternating-optimum-based (AO-based) scheme with a special initialization
method, which is capable to individually provide feasible sub-connected hybrid
precoding (SHP) design. Simulation results highlight the superior spectrum
efficiency of proposed HAC-based FHP scheme, and the high power efficiency of
proposed MKM-based AHP scheme. Moreover, all the proposed schemes are clarified
to effectively handle the inter-user interference and outperform the existing
work
Hybrid Beamforming for mm-Wave Massive MIMO Systems with Partially Connected RF Architecture
To satisfy the capacity requirements of future mobile systems, under-utilized
millimeter wave frequencies can be efficiently exploited by employing massive
MIMO technology with highly directive beamforming. Hybrid analog-digital
beamforming has been recognised as a promising approach for large-scale MIMO
implementations with a reduced number of costly and power-hungry RF chains. In
comparison to fully connected architecture, hybrid beamforming (HBF) with
partially connected RF architecture is particularly appealing for the practical
implementation due to less complex RF power division and combining networks. In
this paper, we first formulate single- and multi-user rate maximization
problems as weighted minimum mean square error (WMMSE) and derive solutions for
hybrid beamformers using alternating optimization. The algorithms are designed
for the full-array- and sub-array-based processing strategies of partially
connected HBF architecture. In addition to the rate maximizing WMMSE solutions,
we propose lower complexity sub-array-based zero-forcing algorithms. The
performance of the proposed algorithms is evaluated in two different channel
models, i.e., a simple geometric model and a realistic statistical millimeter
wave model known as NYUSIM. The performance results of the WMMSE HBF algorithms
are meant to reveal the potential of partially connected HBF and serve as upper
bounds for lower complexity methods. Numerical results imply that properly
designed partially connected HBF has the potential to provide an good
compromise between hardware complexity and system performance in comparison to
fully digital beamforming.Comment: 13 page