Computational Imaging-Based ISAC Method with Large Pixel Division

TL;DR

This paper introduces a novel computational imaging method for environment sensing in millimeter-wave wireless networks, effectively handling large pixel divisions and reducing processing errors in integrated sensing and communication systems.

Contribution

It proposes a new integral form computational imaging model that accounts for continuous surface characteristics and phase differences, extending imaging to large wireless communication scenarios.

Findings

Effective cancellation of errors caused by large pixel division

Extensive numerical results verify the method's effectiveness

First extension of computational imaging to large wireless communication environments

Abstract

One of the key points in designing an integrated sensing and communication (ISAC) system using computational imaging is the division size of imaging pixels. If the size is too small, it leads to a high number of pixels that need processing. On the contrary, it usually causes large processing errors since each pixel is no longer uniformly coherent. In this paper, a novel method is proposed to address such a problem in environment sensing in millimeter-wave wireless cellular networks, which effectively cancels the severe errors caused by large pixel division as in conventional computational imaging algorithms. To this end, a novel computational imaging model in an integral form is introduced, which leverages the continuous characteristics of object surfaces in the environment and takes into account the different phases associated with the different parts of the pixel. The proposed algorithm extends computational imaging to large wireless communication scenarios for the first time. The performance of the proposed method is then analyzed, and extensive numerical results verify its effectiveness.