Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection

doi:10.1109/TVT.2022.3163786

Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection

2022-06-01

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DOI：10.1109/TVT.2022.3163786

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Machine learning has provided great potential in intelligent signal processing, e.g., channel estimation and signal detection. But still, it is difficult to deploy deep neural network (DNN) on low-cost and low-power edge devices, e.g., in both training and inference phases. For one thing, the training of massive network parameters incurs a huge computational complexity and rarely becomes feasible; and for another, even a trained DNN involves still high computations and demands considerable storage size. In this work, we present a tiny machine learning (Tiny-ML) approach for hardware-efficient channel estimation and signal detection. The key innovation of our Tiny-ML is that we replace each large dense layer of DNN with three small cascading sub-layers; and therefore, the computation/storage of a large matrix is replaced with that of small ones. To enable the lightweight training of our Tiny-ML, a novel rank-restricted back-propagation algorithm is further designed. Numerical simulations demonstrate the advantages of our new method. Without sacrificing the estimation and detection accuracy, our Tiny-ML attains an acceleration of 2.5 similar to 3x in model training and similar to 4.5x in model inference, as well as a substantial storage reduction of similar to 4.5x, when compared to classical fully-connected (FC) DNN. As such, our method paves the way for deploying tiny AI onto low-cost and low-power hardware devices, thereby exploiting the full potential of intelligent signal processing.

Keywords:

Intelligent signal processing

hardware-efficient

lightweight

tiny-ML

Tiny Machine Learning (Tiny-ML) for Efficient Channel Estimation and Signal Detection

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