Near-Field Joint Localization and Synchronization
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In 5G communication, linear and planar arrays are used for both positioning and communication. As the arrays grow in size, the conventional far-field assumption is increasingly being violated and curvature of the wavefront should be taken into account. We explicitly contrast near-field and far-field uplink localization performance from a Fisher information perspective and show how a simple algorithm can provide a rough initial estimate for maximum likelihood estimation. Our results show that invoking the more general near-field model allows for joint estimation of a user's location and clock bias in the uplink, while the far-field model may lead to overly pessimistic performance assessment.
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