406 research outputs found
Massive MIMO channel modelling for 5G wireless communication systems
Massive Multiple-Input Multiple-Output (MIMO) wireless communication systems,
equipped with tens or even hundreds of antennas, emerge as a promising technology
for the Fifth Generation (5G) wireless communication networks. To design and evaluate
the performance of massive MIMO wireless communication systems, it is essential
to develop accurate, flexible, and efficient channel models which fully reflect the characteristics
of massive MIMO channels. In this thesis, four massive MIMO channel
models have been proposed.
First, a novel non-stationary wideband multi-confocal ellipse Two-Dimensional (2-D)
Geometry Based Stochastic Model (GBSM) for massive MIMO channels is proposed.
Spherical wavefront is assumed in the proposed channel model, instead of the plane
wavefront assumption used in conventional MIMO channel models. In addition, the
Birth-Death (BD) process is incorporated into the proposed model to capture the
dynamic properties of clusters on both the array and time axes.
Second, we propose a novel theoretical non-stationary Three-Dimensional (3-D) wideband
twin-cluster channel model for massive MIMO communication systems with
carrier frequencies in the order of gigahertz (GHz). As the dimension of antenna arrays
cannot be ignored for massive MIMO, nearfield effects instead of farfield effects
are considered in the proposed model. These include the spherical wavefront assumption
and a BD process to model non-stationary properties of clusters such as cluster
appearance and disappearance on both the array and time axes.
Third, a novel Kronecker Based Stochastic Model (KBSM) for massive MIMO channels
is proposed. The proposed KBSM can not only capture antenna correlations but
also the evolution of scatterer sets on the array axis. In addition, upper and lower
bounds of KBSM channel capacities in both the high and low Signal-to-Noise Ratio
(SNR) regimes are derived when the numbers of transmit and receive antennas are
increasing unboundedly with a constant ratio.
Finally, a novel unified framework of GBSMs for 5G wireless channels is proposed.
The proposed 5G channel model framework aims at capturing key channel characteristics
of certain 5G communication scenarios, such as massive MIMO systems, High
Speed Train (HST) communications, Machine-to-Machine (M2M) communications,
and Milli-meter Wave (mmWave) communications
Downlink Precoding for Massive MIMO Systems Exploiting Virtual Channel Model Sparsity
In this paper, the problem of designing a forward link linear precoder for
Massive Multiple-Input Multiple-Output (MIMO) systems in conjunction with
Quadrature Amplitude Modulation (QAM) is addressed. First, we employ a novel
and efficient methodology that allows for a sparse representation of multiple
users and groups in a fashion similar to Joint Spatial Division and
Multiplexing. Then, the method is generalized to include Orthogonal Frequency
Division Multiplexing (OFDM) for frequency selective channels, resulting in
Combined Frequency and Spatial Division and Multiplexing, a configuration that
offers high flexibility in Massive MIMO systems. A challenge in such system
design is to consider finite alphabet inputs, especially with larger
constellation sizes such as . The proposed methodology is next
applied jointly with the complexity-reducing Per-Group Processing (PGP)
technique, on a per user group basis, in conjunction with QAM modulation and in
simulations, for constellation size up to . We show by numerical results
that the precoders developed offer significantly better performance than the
configuration with no precoder or the plain beamformer and with
Linear Precoding for MIMO Channels with QAM Constellations and Reduced Complexity
In this paper, the problem of designing a linear precoder for Multiple-Input
Multiple-Output (MIMO) systems in conjunction with Quadrature Amplitude
Modulation (QAM) is addressed. First, a novel and efficient methodology to
evaluate the input-output mutual information for a general Multiple-Input
Multiple-Output (MIMO) system as well as its corresponding gradients is
presented, based on the Gauss-Hermite quadrature rule. Then, the method is
exploited in a block coordinate gradient ascent optimization process to
determine the globally optimal linear precoder with respect to the MIMO
input-output mutual information for QAM systems with relatively moderate MIMO
channel sizes. The proposed methodology is next applied in conjunction with the
complexity-reducing per-group processing (PGP) technique, which is
semi-optimal, to both perfect channel state information at the transmitter
(CSIT) as well as statistical channel state information (SCSI) scenarios, with
high transmitting and receiving antenna size, and for constellation size up to
. We show by numerical results that the precoders developed offer
significantly better performance than the configuration with no precoder, and
the maximum diversity precoder for QAM with constellation sizes , and
and for MIMO channel size
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