278 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
6G White Paper on Machine Learning in Wireless Communication Networks
The focus of this white paper is on machine learning (ML) in wireless
communications. 6G wireless communication networks will be the backbone of the
digital transformation of societies by providing ubiquitous, reliable, and
near-instant wireless connectivity for humans and machines. Recent advances in
ML research has led enable a wide range of novel technologies such as
self-driving vehicles and voice assistants. Such innovation is possible as a
result of the availability of advanced ML models, large datasets, and high
computational power. On the other hand, the ever-increasing demand for
connectivity will require a lot of innovation in 6G wireless networks, and ML
tools will play a major role in solving problems in the wireless domain. In
this paper, we provide an overview of the vision of how ML will impact the
wireless communication systems. We first give an overview of the ML methods
that have the highest potential to be used in wireless networks. Then, we
discuss the problems that can be solved by using ML in various layers of the
network such as the physical layer, medium access layer, and application layer.
Zero-touch optimization of wireless networks using ML is another interesting
aspect that is discussed in this paper. Finally, at the end of each section,
important research questions that the section aims to answer are presented
Amplitude Prediction from Uplink to Downlink CSI against Receiver Distortion in FDD Systems
In frequency division duplex (FDD) massive multiple-input multiple-output
(mMIMO) systems, the reciprocity mismatch caused by receiver distortion
seriously degrades the amplitude prediction performance of channel state
information (CSI). To tackle this issue, from the perspective of distortion
suppression and reciprocity calibration, a lightweight neural network-based
amplitude prediction method is proposed in this paper. Specifically, with the
receiver distortion at the base station (BS), conventional methods are employed
to extract the amplitude feature of uplink CSI. Then, learning along the
direction of the uplink wireless propagation channel, a dedicated and
lightweight distortion-learning network (Dist-LeaNet) is designed to restrain
the receiver distortion and calibrate the amplitude reciprocity between the
uplink and downlink CSI. Subsequently, by cascading, a single hidden
layer-based amplitude-prediction network (Amp-PreNet) is developed to
accomplish amplitude prediction of downlink CSI based on the strong amplitude
reciprocity. Simulation results show that, considering the receiver distortion
in FDD systems, the proposed scheme effectively improves the amplitude
prediction accuracy of downlink CSI while reducing the transmission and
processing delay.Comment: 10 pages, 5 figure
HyperRNN: Deep Learning-Aided Downlink CSI Acquisition via Partial Channel Reciprocity for FDD Massive MIMO
In order to unlock the full advantages of massive multiple input multiple
output (MIMO) in the downlink, channel state information (CSI) is required at
the base station (BS) to optimize the beamforming matrices. In frequency
division duplex (FDD) systems, full channel reciprocity does not hold, and CSI
acquisition generally requires downlink pilot transmission followed by uplink
feedback. Prior work proposed the end-to-end design of pilot transmission,
feedback, and CSI estimation via deep learning. In this work, we introduce an
enhanced end-to-end design that leverages partial uplink-downlink reciprocity
and temporal correlation of the fading processes by utilizing jointly downlink
and uplink pilots. The proposed method is based on a novel deep learning
architecture -- HyperRNN -- that combines hypernetworks and recurrent neural
networks (RNNs) to optimize the transfer of long-term channel features from
uplink to downlink. Simulation results demonstrate that the HyperRNN achieves a
lower normalized mean square error (NMSE) performance, and that it reduces
requirements in terms of pilot lengths.Comment: To be presented at SPAWC 202
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
Massive MIMO channel prediction using recurrent neural networks
Massive MIMO has been classified as one of the high potential wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity among others. Due to the exponential increase of connected devices, these properties are of great importance for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) caused by the variations in the channel response due to the presence of highly mobile and rich scattering is a major problem facing massive MIMO systems. Outdated CSI occurs when the information obtained about the channel at the transmitter changes before transmission. This leads to performance degradation of the network. In this work, we demonstrate a low complexity channel prediction method using neural networks. Specifically, we explore the power of recurrent neural network utilizing long-short memory cells in analyzing time series data. We review various neural network-based channel prediction methods available in the literature and compare complexity and performance metrics. Results indicate that the proposed methods outperform conventional systems by tremendously lowering the complexity associated with channel prediction.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E392
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