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Section: Events

Submission Deadline: November 14, 2019 Call for Proposals Document

Section: Publications & Resources

Are you looking to energize signal processing students, early stage researchers, and industry practitioners in your area? Consider hosting a Seasonal School for young engineers near you!

Section: Publications & Resources

Are you looking to energize signal processing students, early stage researchers, and industry practitioners in your area? Consider hosting a Seasonal School for young engineers near you!

Section: Publications & Resources

This webinar, “Direct Localization for Massive MIMO,” will discuss how Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. 

Section: Publications & Resources

This webinar, “Direct Localization for Massive MIMO,” will discuss how Large-scale MIMO systems are well known for their advantages in communications, but they also have the potential for providing very accurate localization, thanks to their high angular resolution. 

We are looking to hire a motivated post-doc to work on machine learning and data analytics. The candidate must have a strong background in machine learning, AI, signal processing, optimization methods, probability, and statistics. The candidate must have a Ph.D. in relevant fields. Required:

Section: Community & Involvement

ScopeThough the IEEE Signal Processing (SPS) and Communications (COMSOC) Societies have evolved somewhat independently, the technical areas covered by the two societies are tightly and undeniably intertwined. In fact, in most problems, it is impossible to tell where the signal processing ends and the communications technology begins, and vice versa.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Nonlinear static multiple-input multiple-output (MIMO) systems are analyzed. The matrix formulation of Bussgang's theorem for complex Gaussian signals is rederived and put in the context of the multivariate cumulant series expansion. The attenuation matrix is a function of the input signals’ covariance and the covariance of the input and output signals.

Section: Publications & Resources

Publication: IEEE Transactions on Signal Processing

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice.