10,676 research outputs found
A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback
Channel state information (CSI) plays a critical role in achieving the
potential benefits of massive multiple input multiple output (MIMO) systems. In
frequency division duplex (FDD) massive MIMO systems, the base station (BS)
relies on sustained and accurate CSI feedback from the users. However, due to
the large number of antennas and users being served in massive MIMO systems,
feedback overhead can become a bottleneck. In this paper, we propose a
model-driven deep learning method for CSI feedback, called learnable
optimization and regularization algorithm (LORA). Instead of using l1-norm as
the regularization term, a learnable regularization module is introduced in
LORA to automatically adapt to the characteristics of CSI. We unfold the
conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural
network and learn both the optimization process and regularization term by
end-toend training. We show that LORA improves the CSI feedback accuracy and
speed. Besides, a novel learnable quantization method and the corresponding
training scheme are proposed, and it is shown that LORA can operate
successfully at different bit rates, providing flexibility in terms of the CSI
feedback overhead. Various realistic scenarios are considered to demonstrate
the effectiveness and robustness of LORA through numerical simulations
Deep Learning for Hybrid Beamforming with Finite Feedback in GSM Aided mmWave MIMO Systems
Hybrid beamforming is widely recognized as an important technique for
millimeter wave (mmWave) multiple input multiple output (MIMO) systems.
Generalized spatial modulation (GSM) is further introduced to improve the
spectrum efficiency. However, most of the existing works on beamforming assume
the perfect channel state information (CSI), which is unrealistic in practical
systems. In this paper, joint optimization of downlink pilot training, channel
estimation, CSI feedback, and hybrid beamforming is considered in GSM aided
frequency division duplexing (FDD) mmWave MIMO systems. With the help of deep
learning, the GSM hybrid beamformers are designed via unsupervised learning in
an end-to-end way. Experiments show that the proposed multi-resolution network
named GsmEFBNet can reach a better achievable rate with fewer feedback bits
compared with the conventional algorithm.Comment: 4 pages, 4 figures. This work has been submitted to the IEEE for
possible publication. Copyright may be transferred without notic
Distributed Space-Time Coding Based on Adjustable Code Matrices for Cooperative MIMO Relaying Systems
An adaptive distributed space-time coding (DSTC) scheme is proposed for
two-hop cooperative MIMO networks. Linear minimum mean square error (MMSE)
receive filters and adjustable code matrices are considered subject to a power
constraint with an amplify-and-forward (AF) cooperation strategy. In the
proposed adaptive DSTC scheme, an adjustable code matrix obtained by a feedback
channel is employed to transform the space-time coded matrix at the relay node.
The effects of the limited feedback and the feedback errors are assessed.
Linear MMSE expressions are devised to compute the parameters of the adjustable
code matrix and the linear receive filters. Stochastic gradient (SG) and
least-squares (LS) algorithms are also developed with reduced computational
complexity. An upper bound on the pairwise error probability analysis is
derived and indicates the advantage of employing the adjustable code matrices
at the relay nodes. An alternative optimization algorithm for the adaptive DSTC
scheme is also derived in order to eliminate the need for the feedback. The
algorithm provides a fully distributed scheme for the adaptive DSTC at the
relay node based on the minimization of the error probability. Simulation
results show that the proposed algorithms obtain significant performance gains
as compared to existing DSTC schemes.Comment: 6 figure
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