91 research outputs found
Advanced wireless communications using large numbers of transmit antennas and receive nodes
The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing
Downlink Extrapolation for FDD Multiple Antenna Systems Through Neural Network Using Extracted Uplink Path Gains
When base stations (BSs) are deployed with multiple antennas, they need to
have downlink (DL) channel state information (CSI) to optimize downlink
transmissions by beamforming. The DL CSI is usually measured at mobile stations
(MSs) through DL training and fed back to the BS in frequency division
duplexing (FDD). The DL training and uplink (UL) feedback might become
infeasible due to insufficient coherence time interval when the channel rapidly
changes due to high speed of MSs. Without the feedback from MSs, it may be
possible for the BS to directly obtain the DL CSI using the inherent relation
of UL and DL channels even in FDD, which is called DL extrapolation. Although
the exact relation would be highly nonlinear, previous studies have shown that
a neural network (NN) can be used to estimate the DL CSI from the UL CSI at the
BS. Most of previous works on this line of research trained the NN using full
dimensional UL and DL channels; however, the NN training complexity becomes
severe as the number of antennas at the BS increases. To reduce the training
complexity and improve DL CSI estimation quality, this paper proposes a novel
DL extrapolation technique using simplified input and output of the NN. It is
shown through many measurement campaigns that the UL and DL channels still
share common components like path delays and angles in FDD. The proposed
technique first extracts these common coefficients from the UL and DL channels
and trains the NN only using the path gains, which depend on frequency bands,
with reduced dimension compared to the full UL and DL channels. Extensive
simulation results show that the proposed technique outperforms the
conventional approach, which relies on the full UL and DL channels to train the
NN, regardless of the speed of MSs.Comment: accepted for IEEE Acces
Common Codebook Millimeter Wave Beam Design: Designing Beams for Both Sounding and Communication with Uniform Planar Arrays
Fifth generation (5G) wireless networks are expected to utilize wide
bandwidths available at millimeter wave (mmWave) frequencies for enhancing
system throughput. However, the unfavorable channel conditions of mmWave links,
e.g., higher path loss and attenuation due to atmospheric gases or water vapor,
hinder reliable communications. To compensate for these severe losses, it is
essential to have a multitude of antennas to generate sharp and strong beams
for directional transmission. In this paper, we consider mmWave systems using
uniform planar array (UPA) antennas, which effectively place more antennas on a
two-dimensional grid. A hybrid beamforming setup is also considered to generate
beams by combining a multitude of antennas using only a few radio frequency
chains. We focus on designing a set of transmit beamformers generating beams
adapted to the directional characteristics of mmWave links assuming a UPA and
hybrid beamforming. We first define ideal beam patterns for UPA structures.
Each beamformer is constructed to minimize the mean squared error from the
corresponding ideal beam pattern. Simulation results verify that the proposed
codebooks enhance beamforming reliability and data rate in mmWave systems.Comment: 14 pages, 10 figure
Alternating Beamforming with Intelligent Reflecting Surface Element Allocation
Intelligent reflecting surface (IRS) has become a promising technology to aid
next generation wireless communication systems. In this paper, we develop an
alternating beamforming technique with a novel concept of IRS element
allocation for multiple-input multiple-output systems when a base station
supports multiple single antenna users aided with a single IRS. Specifically,
we allocate each IRS element separately to each user, thus, in the beamforming
stage allowing the IRS element only consider a single user at a time. In result
to this separation, the complexity is vastly decreased. The proposed
beamforming technique tries to maximize the minimum rate of all users with
minimal complexity. In the numerical results, we show that the proposed
technique is comparable to the convex optimization-based benchmark with
sufficiently less complexity.Comment: 5 pages, 3 figures, submitted to Wireless Communications Letters
(WCL
Radar Imaging Based on IEEE 802.11ad Waveform
The extension to millimeter-wave (mmWave) spectrum of communication frequency
band makes it easy to implement a joint radar and communication system using
single hardware. In this paper, we propose radar imaging based on the IEEE
802.11ad waveform for a vehicular setting. The necessary parameters to be
estimated for inverse synthetic aperture radar (ISAR) imaging are sampled
version of round-trip delay, Doppler shift, and vehicular velocity. The delay
is estimated using the correlation property of Golay complementary sequences
embedded on the IEEE 802.11ad preamble. The Doppler shift is first obtained
from least square estimation using radar return signals and refined by
correcting the phase uncertainty of Doppler shift by phase rotation. The
vehicular velocity is determined from the estimated Doppler shifts and an
equation of motion. Finally, an ISAR image is formed with the acquired
parameters. Simulation results show that it is possible to obtain recognizable
ISAR image from a point scatterer model of a realistic vehicular setting.Comment: 6 pages, 6 figures, and accepted for 2020 IEEE Global Communications
Conference (GLOBECOM
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