DsMLP: A Learning-Based Multi-Layer Perception for MIMO Detection Implemented by Dynamic Stochastic Computing

As the number of antennas increases in multi-input and multi-output (MIMO) systems, even linear detection methods suffer from sharply increasing complexity. This paper proposes a learning-based multi-layer perception (MLP), named dynamic stochastic multi-layer perception (DsMLP), which is implemented by dynamic stochastic computing (DSC). We first establish a similar form between the MLP structure and minimum mean square error (MMSE) matrix operations. Consequently, DsMLP transforms the complex computation problem into an optimization problem of MLP training. Due to the specific design of MLP structure, e.g., same input/output dimension and single layer without activation function, the mathematical representation of DsMLP is identical to the MMSE matrix operations. Therefore, DsMLP guarantees sound model explainability in mathematics, fast convergence in training, and low complexity in computation. Furthermore, we transform the MLP training process to the DSC domain and propose a hardware-efficient scheme for DsMLP. Compared with other state-of-the-art MIMO detectors, DsMLP achieves 1.2× energy efficiency and 1.74× area efficiency.