1,594 research outputs found
Channel Estimation for RIS-aided mmWave Massive MIMO System Using Few-bit ADCs
Millimeter wave (mmWave) massive multiple-input multiple-output (massive
MIMO) is one of the most promising technologies for the fifth generation and
beyond wireless communication system. However, a large number of antennas incur
high power consumption and hardware costs, and high-frequency communications
place a heavy burden on the analog-to-digital converters (ADCs) at the base
station (BS). Furthermore, it is too costly to equipping each antenna with a
high-precision ADC in a large antenna array system. It is promising to adopt
low-resolution ADCs to address this problem. In this paper, we investigate the
cascaded channel estimation for a mmWave massive MIMO system aided by a
reconfigurable intelligent surface (RIS) with the BS equipped with few-bit
ADCs. Due to the low-rank property of the cascaded channel, the estimation of
the cascaded channel can be formulated as a low-rank matrix completion problem.
We introduce a Bayesian optimal estimation framework for estimating the
user-RIS-BS cascaded channel to tackle with the information loss caused by
quantization. To implement the estimator and achieve the matrix completion, we
use efficient bilinear generalized approximate message passing (BiG-AMP)
algorithm. Extensive simulation results verify that our proposed method can
accurately estimate the cascaded channel for the RIS-aided mmWave massive MIMO
system with low-resolution ADCs
A Coordinated Approach to Channel Estimation in Large-scale Multiple-antenna Systems
This paper addresses the problem of channel estimation in multi-cell
interference-limited cellular networks. We consider systems employing multiple
antennas and are interested in both the finite and large-scale antenna number
regimes (so-called "massive MIMO"). Such systems deal with the multi-cell
interference by way of per-cell beamforming applied at each base station.
Channel estimation in such networks, which is known to be hampered by the pilot
contamination effect, constitute a major bottleneck for overall performance. We
present a novel approach which tackles this problem by enabling a low-rate
coordination between cells during the channel estimation phase itself. The
coordination makes use of the additional second-order statistical information
about the user channels, which are shown to offer a powerful way of
discriminating across interfering users with even strongly correlated pilot
sequences. Importantly, we demonstrate analytically that in the
large-number-of-antennas regime, the pilot contamination effect is made to
vanish completely under certain conditions on the channel covariance. Gains
over the conventional channel estimation framework are confirmed by our
simulations for even small antenna array sizes.Comment: 10 pages, 6 figures, to appear in IEEE Journal on Selected Areas in
Communication
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