Skip to main content

Call for Papers: Sparse/Low-rank Tensor Signal Processing for Communication and Radar Systems

To pursue high-quality communication or detection performance, various diversity methodologies have been applied to communication and radar systems, e.g., spatial diversity, temporal diversity, frequency diversity, polarization diversity, etc. Consequently, the transmitted and/or received signals in communication and radar systems always exhibit a multidimensional structure, and can be modeled using tensor algebra tools. In particular, modern communication and radar systems consist of large-scale transmitting/receiving antennas and high speed sampling modules, which leads to a massive volume of data to be processed. On the other hand, the inherent characteristics-of-interesting in the array data are often (or approximately) sparse and low rank. Traditional matrix-based signal processing methods do not fully capture such tensor nature. In comparison, tensor algebra offers fundamental advantages over their matrix counterparts with respect to identifiability and uniqueness. Sparse/low-rank tensor signal processing techniques have been applied in the field of radar and communication systems to mitigate the effect of environmental noise, multi-path propagation, channel inconsistency of receivers and various interferences. These methods can achieve high target detection performance, suppress the background noise and deal with multi-path effect, as well as handle multi-dimensional signals. However, they may also suffer from high computational complexity and model mismatch, etc. Thus, new models and algorithms based on tensor decompositions are needed to improve the performance of communication and radar systems. 

This special issue is intended to solicit high-quality contributions in recent advances for sparse/low-rank tensor signal processing in radar and communication systems. Authors are invited to submit original papers presenting new theoretical and/or application-oriented research including algorithms, models, technology and applications.

The topics of interest for the special issue include, but are not limited to:

  • Advances in tensor decompositions and tensor completion
  • Tensor based methods for channel estimation
  • Tensor based methods for radar applications
  • Tensor based methods for wireless communications
  • Tensor models and for algorithms for MIMO radar
  • Tensor based methods for polarization sensitive arrays
  • Super-resolution for UAV swarms based on tensor signal processing 
  • Quaternion based modeling and processing for communication and radar systems
  • Tensor models and algorithms for joint communication and radar coexistence
  • Tensor based methods for high dimensional parameter estimation
  • Tensor models and applications to 5G and beyond wireless systems
  • Practical implementations of tensor based algorithms

Important Dates
Submission Deadline
: 31 March 2022

Lead Guest Editor
Liangtian Wan, Dalian University of Technology, China

Guest Editors
André L. F. de Almeida, Federal University of Ceará, Brazil
Aohan Li, Tokyo University of Science, Japan

Submission Instructions
Before submitting your manuscript, please ensure you have carefully read the Instructions for Authors for EURASIP Journal on Advances in Signal Processing. The complete manuscript should be submitted through the EURASIP Journal on Advances in Signal Processing submission system. To ensure that you submit to the correct special issue please select the appropriate section in the drop-down menu upon submission. In addition, indicate within your cover letter that you wish your manuscript to be considered as part of the special issue on 'Sparse/Low-rank Tensor Signal Processing for Communication and Radar Systems'. All submissions will undergo rigorous peer review and accepted articles will be published within the journal as a collection.

Sign up for article alerts to keep updated on articles published in EURASIP Journal on Advances in Signal Processing - including articles published in this special issue!