144 research outputs found
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
Joint Port Selection Based Channel Acquisition for FDD Cell-Free Massive MIMO
In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition
of the channel state information (CSI) is very challenging because of the large
overhead required for the training and feedback of the downlink channels of
multiple cooperating base stations (BSs). In this paper, for systems with
partial uplink-downlink channel reciprocity, and a general spatial domain
channel model with variations in the average port power and correlation among
port coefficients, we propose a joint-port-selection-based CSI acquisition and
feedback scheme for the downlink transmission with zero-forcing precoding. The
scheme uses an eigenvalue-decomposition-based transformation to reduce the
feedback overhead by exploring the port correlation. We derive the sum-rate of
the system for any port selection. Based on the sum-rate result, we propose a
low-complexity greedy-search-based joint port selection (GS-JPS) algorithm.
Moreover, to adapt to fast time-varying scenarios, a supervised deep
learning-enhanced joint port selection (DL-JPS) algorithm is proposed.
Simulations verify the effectiveness of our proposed schemes and their
advantage over existing port-selection channel acquisition schemes.Comment: 30 pages, 9 figures. The paper has been submitted to IEEE journal for
possible publicatio
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
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