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arxiveess.SP2026-07-22

Swarm and Evolutionary Computation for Near-Field Localization

Parisa Ramezani, Seyed Jalaleddin Mousavirad, Mattias O'Nils, Emil Björnson

Near-field localization has attracted significant attention in recent years due to the move toward higher frequencies and extremely large aperture arrays, which expand the near-field region and bring many sources into it. This implies that antenna arrays can be used to localize not only in angle but also in range. Although a wide range of localization methods has been developed, each comes with limitations that may hinder practical deployment. This article focuses on a class of techniques that has received relatively little attention in the prior literature despite its strong potential for accurate and efficient location estimation: swarm and evolutionary computation (SEC). These methods are well-suited to the complex optimization landscape of near-field localization and can offer important advantages over conventional approaches such as grid-based subspace methods and deep learning approaches.

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arxiveess.SPphysics.app-ph2026-07-24

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arxiveess.SP2026-07-24

Innovation-Domain Decision-Directed Phase Tracking for Wiener Phase Noise in Fast Rayleigh Fading

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arxiveess.SP2026-07-24

Continuous Intra-Symbol Phase Noise Tracking for THz OFDM via Polynomial Reconstruction

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Future wireless systems will require ever larger antenna arrays and heavier signal processing, making conventional digital multiple-input multiple-output (MIMO) architectures difficult to scale. In this paper, we show that a possible solution is to offload part of the processing…

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