Wireless Communications and Mobile Computing

Scalable and Robust Optimization Techniques for Multidimensional Signal Processing

Publishing date
01 Oct 2022
Submission deadline
27 May 2022

Lead Editor
Guest Editors

1Hainan University, Hainan, China

2National University of Singapore, Singapore

3Kanagawa University, Kanagawa, Japan

4University of Sheffield, Sheffield, UK

This issue is now closed for submissions.

Scalable and Robust Optimization Techniques for Multidimensional Signal Processing

This issue is now closed for submissions.


The machine learning era endows immense opportunities for multidimensional signal processing (MSP), by leveraging big data generated from widespread sensors and ever-growing computing capabilities. Driven by the large amount of data volume, scalable and robust optimization has been thriving with great success and popularity in tackling problems across diverse topics, including direction-of-arrival (DOA) estimation, synthetic-aperture radar (SAR) imaging, unmanned aerial vehicle (UAV) based signal processing, computer vision, robot control system and autonomous driving. For example, with the boost of superior accuracy, deep neural networks (DNN) become deeper and wider in the architecture, and thereby suffer from demanding parameters to challenge the memory access. Low-rank matrix factorization, as a scalable optimization method, is significantly effective to compress DNN in computation and memory. In addition, the increased capabilities of data-driven systems also come along with challenges for efficiently processing, transmitting, and analyzing generated big data, and these challenges are expected to be exacerbated. This is because data-driven systems are almost always trained/learnt on power-hungry modern day supercomputers with costly expensive graphics processing units (GPUs), leading to the limitations of running them on low-powered embedded applications with mingy hardware resources, such as vehicle-mounted radar, mobile and wearable devices. Recently, heterogeneous optimization and acceleration approaches are proposed to reduce the hardware dependency. However, some of them are vulnerable to significant performance degradation in general.

This Special Issue is devoted to investigating efficient and effective optimization methods and algorithms in MSP applications, with the goal to highlight novel research developments in optimization. Although recent research works have shown state-of-the-art performance gains over traditional approaches, compelling research challenges remain still to be addressed, especially when encountering the trade-off between complexity and accuracy. We welcome original research and review articles.

Potential topics include but are not limited to the following:

  • Efficient data preprocessing approach, encoding and feature extracting strategies for MSP
  • Design of novel, scalable and robust optimization methods and algorithms for MSP
  • Scalable and robust optimization technique for MSP applications in SAR imaging, frequency diverse array (FDA)-MIMO radar, UAV, computer vision, robot control system, autonomous driving, etc.
  • Efficiently computational DNN models and architectures for MSP, such as spiking neural networks (SNNs), graph neural networks (GNNs), capsule networks, deep unfolding networks, and their variants
  • Efficient and effective tricks for deep learning for MSP, such as sparsification, quantization, binarization, pruning, matrix/tensor decomposition
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