# Complexity-Scalable Neural Network Based MIMO Detection With Learnable Weight Scaling

arXiv: https://arxiv.org/abs/1909.06943 · HTML: https://mlsanity.com/paper/1909.06943/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection](https://mlsanity.com/paper/2102.00178/) (2021)
- [A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights](https://mlsanity.com/paper/2110.06542/) (2021)
- [Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach](https://mlsanity.com/paper/2111.08110/) (2021)

## Abstract (arXiv)

This paper introduces a framework for systematic complexity scaling of deep neural network(DNN) based MIMO detectors. The model uses a fraction of the DNN inputs by scaling their values through weights that follow monotonically non-increasing functions. This allows for weight scaling across and within the different DNN layers in order to achieve accuracy-vs.-complexity scalability during inference. In order to further improve the performance of our proposal, we introduce a sparsity-inducing regularization constraint in conjunction with trainable weight-scaling functions. In this way, the network learns to balance detection accuracy versus complexity while also increasing robustness to changes in the activation patterns, leading to further improvement in the detection accuracy and BER performance at the same inference complexity. Numerical results show that our approach is 10-foldand 100-fold less complex than classical approaches based on semi-definite relaxation and ML detection, respectively.