Exploiting Interference for Integrated Detection-Communication Waveform Design via Kullback-Leibler Divergence

This paper investigates the design of integrated detection and communication waveforms with a particular focus on the exploitation of constructive interference (CI) at both the system and symbol levels. We formulate a waveform design problem with the objective of optimizing the detection performance which is characterized by the Kullback Leibler distance (KLD) between the probability density functions (PDFs) under two hypotheses, subject to the constraints of the CI condition and constant modulus. To address the challenging non-convex problem, a low-complexity recursive penalty-based Riemannian conjugate gradient (RE-PRCG) algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed algorithm in improving the detection performance and reducing the computational burden over the benchmark.

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Publication Details

Published
2025-03-24
DOI
https://doi.org/10.1109/wcnc61545.2025.10978157
Primary Topic
Distributed Sensor Networks and Detection Algorithms
Type
article
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article

Exploiting Interference for Integrated Detection-Communication Waveform Design via Kullback-Leibler Divergence

Hui Yu, Kai Ying, Xiaqing Diao, Lin Gui
Distributed Sensor Networks and Detection Algorithms
article

Exploiting Interference for Integrated Detection-Communication Waveform Design via Kullback-Leibler Divergence

Hui Yu, Kai Ying, Xiaqing Diao, Lin Gui
article en

Abstract

This paper investigates the design of integrated detection and communication waveforms with a particular focus on the exploitation of constructive interference (CI) at both the system and symbol levels. We formulate a waveform design problem with the objective of optimizing the detection performance which is characterized by the Kullback Leibler distance (KLD) between the probability density functions (PDFs) under two hypotheses, subject to the constraints of the CI condition and constant modulus. To address the challenging non-convex problem, a low-complexity recursive penalty-based Riemannian conjugate gradient (RE-PRCG) algorithm is proposed. The numerical results demonstrate the effectiveness of the proposed algorithm in improving the detection performance and reducing the computational burden over the benchmark.

Shanghai Jiao Tong University (CN)
Openalex Percentile: Top 90%
Distributed Sensor Networks and Detection Algorithms
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