Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

LIU Zhaoting; YU Chen; WANG Yafeng; LIU Shuchen

doi:10.23919/cje.2022.00.272

Volume 33 Issue 1

Jan. 2024

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Article Navigation > Chinese Journal of Electronics > 2024 > 33(1): 153-160

Zhaoting LIU, Chen YU, Yafeng WANG, et al., “Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 153–160, 2024 doi: 10.23919/cje.2022.00.272

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Zhaoting LIU, Chen YU, Yafeng WANG, et al., “Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations,” Chinese Journal of Electronics, vol. 33, no. 1, pp. 153–160, 2024 doi: 10.23919/cje.2022.00.272

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Graph Signal Reconstruction from Low-Resolution Multi-Bit Observations

doi: 10.23919/cje.2022.00.272

1.
School of Communication Engineering, Hangzhou Dianzi University, Hangzhou 310018, China

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Author Bio:
Zhaoting LIU was born in 1975. He received the Ph.D. degree in electrical engineering at Nanjing University of Science and Technology, NanJing, China, in 2011. He is now with the School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China. His current research interests include radar signal processing, wireless sensor network, and adaptive filtering. (Email: liuzht@hdu.edu.cn)

Chen YU was born in 1998. She is an M.S. candidate at the School of Communication Engineering, Hangzhou Dianzi University, China. Her research direction is graph signal processing. (Email: yuchen102955@163.com)

Yafeng WANG was born in 1996. He is an M.S. candidate at the School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, China. His research interests include adaptive filtering and wireless sensor network. (Email: ab203230@126.com)

Shuchen LIU was born in 1999. She is an M.E. candidate at the School of Communication Engineering, Hangzhou Dianzi University, China. His research interests include wireless sensor network, target targeting and tracking. (Email: liushuchen1804@163.com)
Corresponding author: Email: liuzht@hdu.edu.cn
Received Date: 2022-08-12
Accepted Date: 2022-11-22

Available Online: 2022-12-27

Publish Date: 2024-01-05

Abstract

Abstract

Low hardware cost and power consumption in information transmission, processing and storage is an urgent demand for many big data problems, in which the high-dimensional data often be modelled as graph signals. This paper considers the problem of recovering a smooth graph signal by using its low-resolution multi-bit quantized observations. The underlying problem is formulated as a regularized maximum-likelihood optimization and is solved via an expectation maximization scheme. With this scheme, the multi-bit graph signal recovery (MB-GSR) is efficiently implemented by using the quantized observations collected from random subsets of graph nodes. The simulation results show that increasing the sampling resolution to 2 or 3 bits per sample leads to a considerable performance improvement, while the energy consumption and implementation costs remain much lower compared to the implementation of high resolution sampling.
- Graph signal reconstruction,
- Maximum-likelihood,
- Expectation maximization,
- Low-bit quantization

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References(25)

References

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