Learn to Communicate with Neural Calibration: Scalability and Generalization

TL;DR

This paper introduces a neural calibration framework that enhances the scalability and generalization of wireless system design, particularly in massive MIMO networks, by integrating neural networks with traditional algorithms and leveraging system topology.

Contribution

It proposes a novel neural calibration approach that combines deep learning with conventional algorithms, utilizing permutation equivariance for better scalability and generalization in wireless networks.

Findings

Significantly improved scalability over existing methods.

Enhanced generalization to different network configurations.

Superior performance in resource management for massive MIMO.

Abstract

The conventional design of wireless communication systems typically relies on established mathematical models that capture the characteristics of different communication modules. Unfortunately, such design cannot be easily and directly applied to future wireless networks, which will be characterized by large-scale ultra-dense networks whose design complexity scales exponentially with the network size. Furthermore, such networks will vary dynamically in a significant way, which makes it intractable to develop comprehensive analytical models. Recently, deep learning-based approaches have emerged as potential alternatives for designing complex and dynamic wireless systems. However, existing learning-based methods have limited capabilities to scale with the problem size and to generalize with varying network settings. In this paper, we propose a scalable and generalizable neural calibration framework for future wireless system design, where a neural network is adopted to calibrate the input of conventional model-based algorithms. Specifically, the backbone of a traditional time-efficient algorithm is integrated with deep neural networks to achieve a high computational efficiency, while enjoying enhanced performance. The permutation equivariance property, carried out by the topological structure of wireless systems, is furthermore utilized to develop a generalizable neural network architecture. The proposed neural calibration framework is applied to solve challenging resource management problems in massive multiple-input multiple-output (MIMO) systems. Simulation results will show that the proposed neural calibration approach enjoys significantly improved scalability and generalization compared with the existing learning-based methods.