Constructing Deep Neural Networks with a Priori Knowledge of Wireless Tasks

TL;DR

This paper introduces permutation invariant deep neural networks (PINNs) that leverage wireless task properties to reduce training complexity and adapt to input data dimensions, demonstrated through resource allocation and interference coordination tasks.

Contribution

The paper develops a novel permutation invariant DNN architecture that exploits wireless task properties to lower training complexity and enhance adaptability.

Findings

PINNs significantly reduce training complexity.

PINNs adapt flexibly to input data dimensions.

Demonstrated improvements in resource allocation and interference coordination.

Abstract

Deep neural networks (DNNs) have been employed for designing wireless systems in many aspects, say transceiver design, resource optimization, and information prediction. Existing works either use the fully-connected DNN or the DNNs with particular architectures developed in other domains. While generating labels for supervised learning and gathering training samples are time-consuming or cost-prohibitive, how to develop DNNs with wireless priors for reducing training complexity remains open. In this paper, we show that two kinds of permutation invariant properties widely existed in wireless tasks can be harnessed to reduce the number of model parameters and hence the sample and computational complexity for training. We find special architecture of DNNs whose input-output relationships satisfy the properties, called permutation invariant DNN (PINN), and augment the data with the properties. By learning the impact of the scale of a wireless system, the size of the constructed PINNs can flexibly adapt to the input data dimension. We take predictive resource allocation and interference coordination as examples to show how the PINNs can be employed for learning the optimal policy with unsupervised and supervised learning. Simulations results demonstrate a dramatic gain of the proposed PINNs in terms of reducing training complexity.