24,199 research outputs found
Compressed Sensing Based Multi-User Millimeter Wave Systems: How Many Measurements Are Needed?
Millimeter wave (mmWave) systems will likely employ directional beamforming
with large antenna arrays at both the transmitters and receivers. Acquiring
channel knowledge to design these beamformers, however, is challenging due to
the large antenna arrays and small signal-to-noise ratio before beamforming. In
this paper, we propose and evaluate a downlink system operation for multi-user
mmWave systems based on compressed sensing channel estimation and conjugate
analog beamforming. Adopting the achievable sum-rate as a performance metric,
we show how many compressed sensing measurements are needed to approach the
perfect channel knowledge performance. The results illustrate that the proposed
algorithm requires an order of magnitude less training overhead compared with
traditional lower-frequency solutions, while employing mmWave-suitable
hardware. They also show that the number of measurements need to be optimized
to handle the trade-off between the channel estimate quality and the training
overhead.Comment: IEEE International Conference on Acoustics, Speech and Signal
Processing (ICASSP) 201
Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing
Multiple-input multiple-output (MIMO) systems are well suited for
millimeter-wave (mmWave) wireless communications where large antenna arrays can
be integrated in small form factors due to tiny wavelengths, thereby providing
high array gains while supporting spatial multiplexing, beamforming, or antenna
diversity. It has been shown that mmWave channels exhibit sparsity due to the
limited number of dominant propagation paths, thus compressed sensing
techniques can be leveraged to conduct channel estimation at mmWave
frequencies. This paper presents a novel approach of constructing beamforming
dictionary matrices for sparse channel estimation using the continuous basis
pursuit (CBP) concept, and proposes two novel low-complexity algorithms to
exploit channel sparsity for adaptively estimating multipath channel parameters
in mmWave channels. We verify the performance of the proposed CBP-based
beamforming dictionary and the two algorithms using a simulator built upon a
three-dimensional mmWave statistical spatial channel model, NYUSIM, that is
based on real-world propagation measurements. Simulation results show that the
CBP-based dictionary offers substantially higher estimation accuracy and
greater spectral efficiency than the grid-based counterpart introduced by
previous researchers, and the algorithms proposed here render better
performance but require less computational effort compared with existing
algorithms.Comment: 7 pages, 5 figures, in 2017 IEEE International Conference on
Communications Workshop (ICCW), Paris, May 201
Towards Very Large Aperture Massive MIMO: a measurement based study
Massive MIMO is a new technique for wireless communications that claims to
offer very high system throughput and energy efficiency in multi-user
scenarios. The cost is to add a very large number of antennas at the base
station. Theoretical research has probed these benefits, but very few
measurements have showed the potential of Massive MIMO in practice. We
investigate the properties of measured Massive MIMO channels in a large indoor
venue. We describe a measurement campaign using 3 arrays having different shape
and aperture, with 64 antennas and 8 users with 2 antennas each. We focus on
the impact of the array aperture which is the main limiting factor in the
degrees of freedom available in the multiple antenna channel. We find that
performance is improved as the aperture increases, with an impact mostly
visible in crowded scenarios where the users are closely spaced. We also test
MIMO capability within a same user device with user proximity effect. We see a
good channel resolvability with confirmation of the strong effect of the user
hand grip. At last, we highlight that propagation conditions where
line-of-sight is dominant can be favorable
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