# Sparse Array Design for Direction Finding using Deep Learning

arXiv: https://arxiv.org/abs/2308.04615 · HTML: https://mlsanity.com/paper/2308.04615/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 98 references that are in the map.
- First posted on arXiv 8 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [An Introduction to Deep Learning for the Physical Layer](https://mlsanity.com/paper/1702.00832/) (2017)
- [Manopt, a Matlab toolbox for optimization on manifolds](https://mlsanity.com/paper/1308.5200/) (2013)
- [DeepMUSIC: Multiple Signal Classification via Deep Learning](https://mlsanity.com/paper/1912.04357/) (2019)
- [Joint Antenna Selection and Hybrid Beamformer Design using Unquantized and Quantized Deep Learning Networks](https://mlsanity.com/paper/1905.03107/) (2019)
- [Twenty-Five Years of Advances in Beamforming: From Convex and Nonconvex Optimization to Learning Techniques](https://mlsanity.com/paper/2211.02165/) (2022)
- [Toward Millimeter Wave Joint Radar-Communications: A Signal Processing Perspective](https://mlsanity.com/paper/1905.00690/) (2019)
- [Cognitive Radar Antenna Selection via Deep Learning](https://mlsanity.com/paper/1802.09736/) (2018)
- [Terahertz-Band Joint Ultra-Massive MIMO Radar-Communications: Model-Based and Model-Free Hybrid Beamforming](https://mlsanity.com/paper/2103.00328/) (2021)
- [Sparse Array Selection Across Arbitrary Sensor Geometries with Deep Transfer Learning](https://mlsanity.com/paper/2004.11637/) (2020)
- [Co-Pulsing FDA Radar](https://mlsanity.com/paper/2201.07107/) (2022)

## Abstract (arXiv)

In the past few years, deep learning (DL) techniques have been introduced for designing sparse arrays. These methods offer the advantages of feature engineering and low prediction-stage complexity, which is helpful in tackling the combinatorial search inherent to finding a sparse array. In this chapter, we provide a synopsis of several direction finding applications of DL-based sparse arrays. We begin by examining supervised and transfer learning techniques that have applications in selecting sparse arrays for a cognitive radar application. Here, we also discuss the use of meta-heuristic learning algorithms such as simulated annealing for the case of designing two-dimensional sparse arrays. Next, we consider DL-based antenna selection for wireless communications, wherein sparse array problem may also be combined with channel estimation, beamforming, or localization. Finally, we provide an example of deep sparse array technique for integrated sensing and communications (ISAC) application, wherein a trade-off of radar and communications performance makes ISAC sparse array problem very challenging. For each setting, we illustrate the performance of model-based optimization and DL techniques through several numerical experiments. We discuss additional considerations required to ensure robustness of DL-based algorithms against various imperfections in array data.