1,624 research outputs found
Parametric channel estimation for massive MIMO
Channel state information is crucial to achieving the capacity of
multi-antenna (MIMO) wireless communication systems. It requires estimating the
channel matrix. This estimation task is studied, considering a sparse channel
model particularly suited to millimeter wave propagation, as well as a general
measurement model taking into account hybrid architectures. The contribution is
twofold. First, the Cram{\'e}r-Rao bound in this context is derived. Second,
interpretation of the Fisher Information Matrix structure allows to assess the
role of system parameters, as well as to propose asymptotically optimal and
computationally efficient estimation algorithms
Doubly Massive mmWave MIMO Systems: Using Very Large Antenna Arrays at Both Transmitter and Receiver
One of the key features of next generation wireless communication systems
will be the use of frequencies in the range 10-100GHz (aka mmWave band) in
densely populated indoor and outdoor scenarios. Due to the reduced wavelength,
antenna arrays with a large number of antennas can be packed in very small
volumes, making thus it possible to consider, at least in principle,
communication links wherein not only the base-station, but also the user
device, are equipped with very large antenna arrays. We denote this
configuration as a "doubly-massive" MIMO wireless link. This paper introduces
the concept of doubly massive MIMO systems at mmWave, showing that at mmWave
the fundamentals of the massive MIMO regime are completely different from what
happens at conventional sub-6 GHz cellular frequencies. It is shown for
instance that the multiplexing capabilities of the channel and its rank are no
longer ruled by the number of transmit and receive antennas, but rather by the
number of scattering clusters in the surrounding environment. The implications
of the doubly massive MIMO regime on the transceiver processing, on the system
energy efficiency and on the system throughput are also discussed.Comment: Accepted for presentation at 2016 IEEE GLOBECOM, Washington (DC),
USA, December 201
Deep Learning Aided Parametric Channel Covariance Matrix Estimation for Millimeter Wave Hybrid Massive MIMO
Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher
than those being used in previous wireless communications systems, are utilized
to meet the increased throughput requirements that come with 5G communications.
The high levels of attenuation experienced by electromagnetic waves in these
frequencies causes MIMO channels to have high spatial correlation. To attain
desirable error performances, systems require knowledge about the channel
correlations. In this thesis, a deep neural network aided method is proposed
for the parametric estimation of the channel covariance matrix (CCM), which
contains information regarding the channel correlations. When compared to some
methods found in the literature, the proposed method yields satisfactory
performance in terms of both computational complexity and channel estimation
errors.Comment: M.Sc. Thesis, published at:
https://open.metu.edu.tr/handle/11511/9319
On the Impact of Hardware Impairments on Massive MIMO
Massive multi-user (MU) multiple-input multiple-output (MIMO) systems are one
possible key technology for next generation wireless communication systems.
Claims have been made that massive MU-MIMO will increase both the radiated
energy efficiency as well as the sum-rate capacity by orders of magnitude,
because of the high transmit directivity. However, due to the very large number
of transceivers needed at each base-station (BS), a successful implementation
of massive MU-MIMO will be contingent on of the availability of very cheap,
compact and power-efficient radio and digital-processing hardware. This may in
turn impair the quality of the modulated radio frequency (RF) signal due to an
increased amount of power-amplifier distortion, phase-noise, and quantization
noise.
In this paper, we examine the effects of hardware impairments on a massive
MU-MIMO single-cell system by means of theory and simulation. The simulations
are performed using simplified, well-established statistical hardware
impairment models as well as more sophisticated and realistic models based upon
measurements and electromagnetic antenna array simulations.Comment: 7 pages, 9 figures, Accepted for presentation at Globe-Com workshop
on Massive MIM
Subspace Tracking and Least Squares Approaches to Channel Estimation in Millimeter Wave Multiuser MIMO
The problem of MIMO channel estimation at millimeter wave frequencies, both
in a single-user and in a multi-user setting, is tackled in this paper. Using a
subspace approach, we develop a protocol enabling the estimation of the right
(resp. left) singular vectors at the transmitter (resp. receiver) side; then,
we adapt the projection approximation subspace tracking with deflation and the
orthogonal Oja algorithms to our framework and obtain two channel estimation
algorithms. We also present an alternative algorithm based on the least squares
approach. The hybrid analog/digital nature of the beamformer is also explicitly
taken into account at the algorithm design stage. In order to limit the system
complexity, a fixed analog beamformer is used at both sides of the
communication links. The obtained numerical results, showing the accuracy in
the estimation of the channel matrix dominant singular vectors, the system
achievable spectral efficiency, and the system bit-error-rate, prove that the
proposed algorithms are effective, and that they compare favorably, in terms of
the performance-complexity trade-off, with respect to several competing
alternatives.Comment: To appear on the IEEE Transactions on Communication
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