# Structure of Deep Neural Networks with a Priori Information in Wireless Tasks

arXiv: https://arxiv.org/abs/1910.13728 · HTML: https://mlsanity.com/paper/1910.13728/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2019.

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

- [Distributed Learning for Time-varying Networks: A Scalable Design](https://mlsanity.com/paper/2108.00231/) (2021)
- [Knowledge-Driven Multi-Agent Reinforcement Learning for Computation Offloading in Cybertwin-Enabled Internet of Vehicles](https://mlsanity.com/paper/2308.02603/) (2023)
- [Precoder Learning by Leveraging Unitary Equivariance Property](https://mlsanity.com/paper/2503.09398/) (2025)

## Abstract (arXiv)

Deep neural networks (DNNs) have been employed for designing wireless networks in many aspects, such as transceiver optimization, resource allocation, and information prediction. Existing works either use fully-connected DNN or the DNNs with specific structures that are designed in other domains. In this paper, we show that a priori information widely existed in wireless tasks is permutation invariant. For these tasks, we propose a DNN with special structure, where the weight matrices between layers of the DNN only consist of two smaller sub-matrices. By such way of parameter sharing, the number of model parameters reduces, giving rise to low sample and computational complexity for training a DNN. We take predictive resource allocation as an example to show how the designed DNN can be applied for learning the optimal policy with unsupervised learning. Simulations results validate our analysis and show dramatic gain of the proposed structure in terms of reducing training complexity.