5,665 research outputs found
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
Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM
Channel estimation and signal detection are very challenging for an
orthogonal frequency division multiplexing (OFDM) system without cyclic prefix
(CP). In this article, deep learning based on orthogonal approximate message
passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver
includes a channel estimation neural network (CE-Net) and a signal detection
neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the
least square channel estimation algorithm and refined by minimum mean-squared
error (MMSE) neural network. The OAMP-Net is established by unfolding the
iterative OAMP algorithm and adding some trainable parameters to improve the
detection performance. The DL-OAMP receiver is with low complexity and can
estimate time-varying channels with only a single training. Simulation results
demonstrate that the bit-error rate (BER) of the proposed scheme is lower than
those of competitive algorithms for high-order modulation.Comment: 5 pages, 4 figures, updated manuscript, International Conference on
Acoustics, Speech and Signal Processing (ICASSP 2019). arXiv admin note:
substantial text overlap with arXiv:1903.0476
Leveraging Vision Reconstruction Pipelines for Satellite Imagery
Reconstructing 3D geometry from satellite imagery is an important topic of
research. However, disparities exist between how this 3D reconstruction problem
is handled in the remote sensing context and how multi-view reconstruction
pipelines have been developed in the computer vision community. In this paper,
we explore whether state-of-the-art reconstruction pipelines from the vision
community can be applied to the satellite imagery. Along the way, we address
several challenges adapting vision-based structure from motion and multi-view
stereo methods. We show that vision pipelines can offer competitive speed and
accuracy in the satellite context.Comment: Project Page: https://kai-46.github.io/VisSat
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