Convex optimization-based beamforming

  • Is convex optimization important?

    Convex optimization has become an essential tool in machine learning because many real-world problems can be modeled as convex optimization problems.
    For example, in classification problems, the goal is to find the best hyperplane that separates the data points into different classes..

It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems. Illustration of receive adaptive beamforming: Polar plot of adapted beampattern.

What is a convolutional beamformer?

Convolutional beamformers are gaining salience in acoustics and ultrasound as a means to combine multiple, usually non-linear, operations with beamforming

There is also recent interest in beamforming for biomimetic antenna arrays that are based on the direction binaural mechanism of humans or animals [54, 55]

What is nonconvex beamforming?

In nonconvex beamforming, each of the problem settings imposes different constraints on, e

g

, PSDness (general-rank beamforming), the probability distri- bution (chance-constrained robust beamforming), constant- modulus (hybrid beamforming), and received SNR (multicast beamforming)

Each learning algorithm offers specific advantages of its own

Why is convex optimization important for beamforming?

It is demonstrated that convex optimization provides an indispensable set of tools for beamforming, enabling rigorous formulation and effective solution of both long-standing and emerging design problems


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