749 research outputs found
Performance Analysis of Channel Extrapolation in FDD Massive MIMO Systems
Channel estimation for the downlink of frequency division duplex (FDD)
massive MIMO systems is well known to generate a large overhead as the amount
of training generally scales with the number of transmit antennas in a MIMO
system. In this paper, we consider the solution of extrapolating the channel
frequency response from uplink pilot estimates to the downlink frequency band,
which completely removes the training overhead. We first show that conventional
estimators fail to achieve reasonable accuracy. We propose instead to use
high-resolution channel estimation. We derive theoretical lower bounds (LB) for
the mean squared error (MSE) of the extrapolated channel. Assuming that the
paths are well separated, the LB is simplified in an expression that gives
considerable physical insight. It is then shown that the MSE is inversely
proportional to the number of receive antennas while the extrapolation
performance penalty scales with the square of the ratio of the frequency offset
and the training bandwidth. The channel extrapolation performance is validated
through numeric simulations and experimental measurements taken in an anechoic
chamber. Our main conclusion is that channel extrapolation is a viable solution
for FDD massive MIMO systems if accurate system calibration is performed and
favorable propagation conditions are present.Comment: arXiv admin note: substantial text overlap with arXiv:1902.0684
A Generalized Framework on Beamformer Design and CSI Acquisition for Single-Carrier Massive MIMO Systems in Millimeter Wave Channels
In this paper, we establish a general framework on the reduced dimensional
channel state information (CSI) estimation and pre-beamformer design for
frequency-selective massive multiple-input multiple-output MIMO systems
employing single-carrier (SC) modulation in time division duplex (TDD) mode by
exploiting the joint angle-delay domain channel sparsity in millimeter (mm)
wave frequencies. First, based on a generic subspace projection taking the
joint angle-delay power profile and user-grouping into account, the reduced
rank minimum mean square error (RR-MMSE) instantaneous CSI estimator is derived
for spatially correlated wideband MIMO channels. Second, the statistical
pre-beamformer design is considered for frequency-selective SC massive MIMO
channels. We examine the dimension reduction problem and subspace (beamspace)
construction on which the RR-MMSE estimation can be realized as accurately as
possible. Finally, a spatio-temporal domain correlator type reduced rank
channel estimator, as an approximation of the RR-MMSE estimate, is obtained by
carrying out least square (LS) estimation in a proper reduced dimensional
beamspace. It is observed that the proposed techniques show remarkable
robustness to the pilot interference (or contamination) with a significant
reduction in pilot overhead
Massive MU-MIMO Downlink TDD Systems with Linear Precoding and Downlink Pilots
We consider a massive MU-MIMO downlink time-division duplex system where a
base station (BS) equipped with many antennas serves several single-antenna
users in the same time-frequency resource. We assume that the BS uses linear
precoding for the transmission. To reliably decode the signals transmitted from
the BS, each user should have an estimate of its channel. In this work, we
consider an efficient channel estimation scheme to acquire CSI at each user,
called beamforming training scheme. With the beamforming training scheme, the
BS precodes the pilot sequences and forwards to all users. Then, based on the
received pilots, each user uses minimum mean-square error channel estimation to
estimate the effective channel gains. The channel estimation overhead of this
scheme does not depend on the number of BS antennas, and is only proportional
to the number of users. We then derive a lower bound on the capacity for
maximum-ratio transmission and zero-forcing precoding techniques which enables
us to evaluate the spectral efficiency taking into account the spectral
efficiency loss associated with the transmission of the downlink pilots.
Comparing with previous work where each user uses only the statistical channel
properties to decode the transmitted signals, we see that the proposed
beamforming training scheme is preferable for moderate and low-mobility
environments.Comment: Allerton Conference on Communication, Control, and Computing,
Urbana-Champaign, Illinois, Oct. 201
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