Neural Network-Based Fixed-Complexity Precoder Selection for Multiple Antenna Systems

In this paper, we propose a neural network-based precoder selection method for multiple antenna systems that are equipped with maximum likelihood detectors. We train a fully connected neural network by supervised learning with novel soft labels that are derived from the error probability of maximum...

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Bibliographic Details
Published in:IEEE access Vol. 10; pp. 120343 - 120351
Main Authors: Kim, Jaekwon, Lim, Hyo-Sang
Format: Journal Article
Language:English
Published: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
Antennas
Complexity
Complexity theory
fixed complexity
Hardware
Maximum likelihood detection
MIMO communication
multiple antenna
network pruning
neural network
Neural networks
Optimization
Precoder selection
Receiving antennas
Regularization
Regularization methods
Transmitting antennas
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:In this paper, we propose a neural network-based precoder selection method for multiple antenna systems that are equipped with maximum likelihood detectors. We train a fully connected neural network by supervised learning with novel soft labels that are derived from the error probability of maximum likelihood detection. The dimension of the input data is reduced by QR decomposition of the channel matrices, thereby reducing the number of nodes of the input layer. Furthermore, the dimension reduction improves the network accuracy. The number of connections between the layers are reduced by applying the network pruning technique, after which the surviving connections are retrained to recover the degraded accuracy due to the pruning. We also optimize the regularization method, considering not only network overfitting but also pruning and retraining. Our method achieves a near optimal bit error performance of the previous sphere decoding (SD)-based symbolic algorithm, of which complexity fluctuates depending on channel matrices. Unlike the conventional SD-based method, the complexity of the proposed method is fixed by the intrinsic characteristic of neural network, which is desirable from the perspective of hardware implementation. And the fixed complexity is lowered by pruning unimportant connections of the networks. With the aid of computer simulations, we show that the fixed complexity of the proposed method is close to the average complexity of the conventional SD-based symbolic algorithm, allowing only negligible degradation of the error performance.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2022.3221800