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IEEE Signal Processing Society Blog


The SPS blog aims to raise awareness about signal processing and Society-related topics to a general interest audience in an engaging, informal, and non-technical way. If you're interested in contributing to the SPS blog, please contact the SPS Blog Team at sps-blog@ieee.org for more information.

Learning the MMSE Channel Estimator

By: 
David Neumann, Thomas Wiese, Wolfgang Utschick

Accurate channel estimation is a major challenge in the next generation of wireless communication networks. To fully exploit setups with many antennas, estimation errors must be kept small. This can be achieved by exploiting the structure inherent in the channel vectors. For example, line-of-sight paths result in highly correlated channel coefficients.

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Graph Neural Networks

By: 
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro

Filtering is the fundamental operation upon which the field of signal processing is built. Loosely speaking, filtering is a mapping between signals, typically used to extract useful information (output signal) from data (input signal). Arguably, the most popular type of filter is the linear and shift-invariant (i.e. independent of the starting point of the signal) filter, which can be computed efficiently by leveraging the convolution operation. 

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Hybrid Beamforming for 5G Millimeter-Wave Systems

By: 
Dr. Xianghao Yu and Dr. Jun Zhang

The upcoming 5G network needs to achieve substantially larger link capacity and ultra-low latency to support emerging mobile applications. While conventional techniques have reached their limits, uplifting the carrier frequency to the millimeter-wave (mm-wave) band stands out as an effective approach to further boost the network capacity, as it provides orders of magnitude greater spectrum than current cellular bands.

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How to Realize Data Sharing With Sensitive Information Hiding in Remote Data Integrity Auditing

By: 
Ms. Wenting Shen, Prof. Jing Qin, and Prof. Jiankun Hu

With the explosive growth of data, it is a heavy burden for users to store the sheer amount of data locally. Therefore, more and more organizations and individuals would like to store their data in the cloud. However, the data stored in the cloud might be corrupted or lost due to the inevitable software bugs, hardware faults and human errors in the cloud. 

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Adaptive Importance Sampling: The Past, the Present, and the Future

By: 
Dr. Victor Elvira

Monte Carlo (MC) methods are a set of fascinating computational techniques that have attracted ever-increasing attention in the last decades. They are based on the simulation of random samples that are used for diverse purposes, such as numerical integration or optimization.

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Deep Learning on Graphs: History, Successes, Challenges, and Next Steps

By: 
Michael Bronstein

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.

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Artificial Intelligence in Radio Frequencies

By: 
Fatemeh Shah Mohammadi

Artificial intelligence (AI) and machine learning (ML) as an application of AI, has today become an inevitable part of major industries such as healthcare, financial trending, and transportation. Future urgent need to intelligently utilize wireless resources to meet the need of ever-increasing diversity in services and user behavior, has actuated the wireless communication industry to deploy AI and ML techniques.

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SPS Technical Committee Spotlight - Erik Meijering

Erik Meijering, the current Chair of SPS’s Bio Imaging and Signal Processing (BISP) Technical Committee, has been pursuing his passion in signal processing from the onset of his education. Realizing early on the vast and evolving ways that signal processing can unlock solutions for the advancement of technology, Erik’s studies began in digital image processing and eventually focused on applications in biomedical imaging for his PhD.

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What Is the Future of Medical Imaging Technology?

If you’ve ever been to the emergency room due to an injury, chances are you’ve had a routine encounter with what is in fact a wonder of the modern age: Magnetic Resonance Imaging, or MRI. Advancements in medical imaging allow us to study parts of the body, including the brain, to an extent unprecedented in the history of medical science.

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Meet an Engineer Whose Work Optimizes Your Streaming Video Experience

By: 
Interview with Alan Bovik

Alan Bovik has a storied career. An American engineer, vision scientist and Primetime Emmy Award winner, Bovik holds the Cockrell Family Endowed Regents Chair in Engineering at The University of Texas at Austin, where he has been the Director of the Laboratory for Image and Video Engineering for more than three decades.

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