128 research outputs found
MIMO Channel Information Feedback Using Deep Recurrent Network
In a multiple-input multiple-output (MIMO) system, the availability of
channel state information (CSI) at the transmitter is essential for performance
improvement. Recent convolutional neural network (NN) based techniques show
competitive ability in realizing CSI compression and feedback. By introducing a
new NN architecture, we enhance the accuracy of quantized CSI feedback in MIMO
communications. The proposed NN architecture invokes a module named long
short-term memory (LSTM) which admits the NN to benefit from exploiting
temporal and frequency correlations of wireless channels. Compromising
performance with complexity, we further modify the NN architecture with a
significantly reduced number of parameters to be trained. Finally, experiments
show that the proposed NN architectures achieve better performance in terms of
both CSI compression and recovery accuracy
Jointly Sparse Support Recovery via Deep Auto-encoder with Applications in MIMO-based Grant-Free Random Access for mMTC
In this paper, a data-driven approach is proposed to jointly design the
common sensing (measurement) matrix and jointly support recovery method for
complex signals, using a standard deep auto-encoder for real numbers. The
auto-encoder in the proposed approach includes an encoder that mimics the noisy
linear measurement process for jointly sparse signals with a common sensing
matrix, and a decoder that approximately performs jointly sparse support
recovery based on the empirical covariance matrix of noisy linear measurements.
The proposed approach can effectively utilize the feature of common support and
properties of sparsity patterns to achieve high recovery accuracy, and has
significantly shorter computation time than existing methods. We also study an
application example, i.e., device activity detection in Multiple-Input
Multiple-Output (MIMO)-based grant-free random access for massive machine type
communications (mMTC). The numerical results show that the proposed approach
can provide pilot sequences and device activity detection with better detection
accuracy and substantially shorter computation time than well-known recovery
methods.Comment: 5 pages, 8 figures, to be publised in IEEE SPAWC 2020. arXiv admin
note: text overlap with arXiv:2002.0262
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