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A closed-form approximation of correlated multiuser MIMO ergodic capacity with antenna selection and imperfect channel estimation
Abstract—Antenna selection (AS) is a promising technology to substantially reduce the complexity of massive multiple-input multiple-output (MIMO) systems. However, spatial correlation and imperfect estimation of channel state information (CSI) are well known to have a direct impact on the capacity of feasible MIMO schemes. In this paper, a tight closed-form approximation of the ergodic capacity for correlated Rayleigh fading multiuser
MIMO channels with receive AS and imperfect CSI is presented. The derived expression takes into account the spatial correlation at both link sides and channel estimation error at the receiver. It can be used for arbitrary numbers of users, antennas, and receive
RF chains. Furthermore, a concise analytical capacity formula is derived in the high signal-to-noise ratio (SNR) region. Numerical results validate the accuracy of our closed-form expressions over different channel conditions and SNRs. The new capacity approximation extends the state-of-the-art and enables efficient
performance evaluation of varied multiantenna applications including massive MIMO for 5G systems
Kronecker Product Correlation Model and Limited Feedback Codebook Design in a 3D Channel Model
A 2D antenna array introduces a new level of control and additional degrees
of freedom in multiple-input-multiple-output (MIMO) systems particularly for
the so-called "massive MIMO" systems. To accurately assess the performance
gains of these large arrays, existing azimuth-only channel models have been
extended to handle 3D channels by modeling both the elevation and azimuth
dimensions. In this paper, we study the channel correlation matrix of a generic
ray-based 3D channel model, and our analysis and simulation results demonstrate
that the 3D correlation matrix can be well approximated by a Kronecker
production of azimuth and elevation correlations. This finding lays the
theoretical support for the usage of a product codebook for reduced complexity
feedback from the receiver to the transmitter. We also present the design of a
product codebook based on Grassmannian line packing.Comment: 6 pages, 5 figures, to appear at IEEE ICC 201
Channel Estimation and Uplink Achievable Rates in One-Bit Massive MIMO Systems
This paper considers channel estimation and achievable rates for the uplink
of a massive multiple-input multiple-output (MIMO) system where the base
station is equipped with one-bit analog-to-digital converters (ADCs). By
rewriting the nonlinear one-bit quantization using a linear expression, we
first derive a simple and insightful expression for the linear minimum
mean-square-error (LMMSE) channel estimator. Then employing this channel
estimator, we derive a closed-form expression for the lower bound of the
achievable rate for the maximum ratio combiner (MRC) receiver. Numerical
results are presented to verify our analysis and show that our proposed LMMSE
channel estimator outperforms the near maximum likelihood (nML) estimator
proposed previously.Comment: 5 pages, 2 figures, the Ninth IEEE Sensor Array and Multichannel
Signal Processing Worksho
Power Scaling of Uplink Massive MIMO Systems with Arbitrary-Rank Channel Means
This paper investigates the uplink achievable rates of massive multiple-input
multiple-output (MIMO) antenna systems in Ricean fading channels, using
maximal-ratio combining (MRC) and zero-forcing (ZF) receivers, assuming perfect
and imperfect channel state information (CSI). In contrast to previous relevant
works, the fast fading MIMO channel matrix is assumed to have an arbitrary-rank
deterministic component as well as a Rayleigh-distributed random component. We
derive tractable expressions for the achievable uplink rate in the
large-antenna limit, along with approximating results that hold for any finite
number of antennas. Based on these analytical results, we obtain the scaling
law that the users' transmit power should satisfy, while maintaining a
desirable quality of service. In particular, it is found that regardless of the
Ricean -factor, in the case of perfect CSI, the approximations converge to
the same constant value as the exact results, as the number of base station
antennas, , grows large, while the transmit power of each user can be scaled
down proportionally to . If CSI is estimated with uncertainty, the same
result holds true but only when the Ricean -factor is non-zero. Otherwise,
if the channel experiences Rayleigh fading, we can only cut the transmit power
of each user proportionally to . In addition, we show that with an
increasing Ricean -factor, the uplink rates will converge to fixed values
for both MRC and ZF receivers
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