Learn to Communicate With Neural Calibration : Scalability and Generalization

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

3 Scopus Citations
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  • Yifan Ma
  • Yifei Shen
  • Jun Zhang
  • S. H. Song
  • Khaled B. Letaief


Original languageEnglish
Pages (from-to)9947-9961
Journal / PublicationIEEE Transactions on Wireless Communications
Issue number11
Online published14 Jun 2022
Publication statusPublished - Nov 2022
Externally publishedYes


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.

Research Area(s)

  • Deep learning, massive MIMO wireless communications, model-based method, permutation equivariance, scalability

Citation Format(s)

Learn to Communicate With Neural Calibration: Scalability and Generalization. / Ma, Yifan; Shen, Yifei; Yu, Xianghao et al.
In: IEEE Transactions on Wireless Communications, Vol. 21, No. 11, 11.2022, p. 9947-9961.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review