222 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
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
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
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