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Harnessing Tensor Structures-Multi-Mode Reservoir Computing and Its Application in Massive MIMO
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DOI：10.1109/TWC.2022.3164203.png)
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摘要
En 中文
In this paper, we introduce a new neural network (NN) structure, multi-mode reservoir computing (Multi-Mode RC). It inherits the dynamic mechanism of RC and processes the forward path and loss optimization of the NN using tensor as the underlying data format. Multi-Mode RC exhibits less complexity compared to conventional RC structures (e.g. single-mode RC), and offers comparable generalization performance to its single-mode counterpart. Furthermore, we introduce an alternating least square-based learning algorithm as well as the associated theoretical analysis for Multi-Mode RC. The result can be utilized to guide the configuration of NN parameters to sufficiently circumvent over-fitting issues. As a key application, we consider the symbol detection task in multiple-input-multiple-output (MIMO) orthogonal-frequency-division-multiplexing (OFDM) systems with massive MIMO employed at the base stations (BSs). Thanks to the tensor structure of massive MIMO-OFDM signals, our online learning-based symbol detection method generalizes well in terms of bit error rate even using a limited online training set. Evaluation results suggest that the Multi-Mode RC-based learning framework can efficiently and effectively combat practical constraints of wireless systems (i.e. channel state information (CSI) errors and hardware non-linearity) to enable robust and adaptive communications over the air.
Keyword:
Symbols
Artificial neural networks
Tensors
Massive MIMO
Training
5G mobile communication
Wireless communication
Reservoir computing
neural networks
online training
massive MIMO
5G
imperfect CSI
and non-linearity
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
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