Evidence map›Paper›PMID 40942907›Full record

ArticleSensors (Basel, Switzerland)2025

Performance Analysis of OCDM in ISAC Scenario.

Pengfei Xu, Mao Li, Tao Zhan, Fengkui Gong, Yue Xiao, Xia Lei

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Pengfei XuSchool of Telecommunications Engineering, Xidian University, Xi'an 710071, China.
Mao LiNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Tao ZhanNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Fengkui GongSchool of Telecommunications Engineering, Xidian University, Xi'an 710071, China.
Yue XiaoNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.
Xia LeiNational Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid evolution of communication systems, exemplified by the Internet of Things (IoT), demands increasingly stringent reliability in both communication and sensing. While Orthogonal Frequency Division Multiplexing (OFDM) struggles to meet the challenges posed by complex scenarios, Orthogonal Chirp Division Multiplexing (OCDM) has gained attention for its robustness and spectral efficiency in Integrated Sensing and Communication (ISAC) systems. However, its sensing mechanism remains insufficiently explored. This paper presents a theoretical analysis of the communication and sensing performance of OCDM waveforms within the ISAC framework. Specifically, a closed-form BER expression under equalization is derived, alongside the ambiguity function and detection performance evaluation under matched filter (MF) and Generalized Likelihood Ratio Test (GLRT) detectors with a constant false alarm rate (CFAR) criterion. Simulation results demonstrate that OCDM offers comparable sensing performance to OFDM while achieving superior communication robustness in complex environments.

Indexed as

Integrated Sensing and Communication (ISAC)Orthogonal Chirp Division Multiplexing (OCDM)Orthogonal Frequency Division Multiplexing (OFDM)

Identifiers

PMID40942907
PMCPMC12431190

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.