Evidence map›Paper›PMID 42515383›Full record

ArticleSensors (Basel, Switzerland)2026

A Symmetric ICA-Based CDMA Receiver for Dense IoT-Enabled Healthcare Monitoring Systems.

Muhammad Irfan Anjum, Abdullah Waqas, Asad Saleem

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

3 authors.

Muhammad Irfan AnjumFaculty of IT, University of Central Punjab, Lahore 54000, Pakistan.ORCID 0000-0003-2539-1848
Abdullah WaqasDepartment of Electrical Engineering, National University of Technology, Islamabad 44000, Pakistan.ORCID 0000-0002-8917-460X
Asad SaleemSchool of Electronic Engineering and Intelligent Manufacturing, Anhui Xinhua University, Hefei 230088, China.ORCID 0000-0001-6739-0101

Funding

Anhui Provincial Department of Education 2025AHGXZK31529Anhui Xinhua University Research Start-Up Fund for Young Doctoral Research 2026kyqd18Key Project of the School of Electronic Engineering and Intelligent Manufacturing, Anhui Xinhua University 2025zrzdi06National Natural Science Foundation of China W2433153
6 · The paper itself

Abstract

The rapid development of Internet of Things (IoT)-enabled healthcare systems, including wearable medical sensors and remote patient monitoring devices, has led to dense multiuser communication scenarios in which numerous low-power devices simultaneously transmit physiological data. In such environments, multiuser interference significantly degrades reliability and increases error rates, which may compromise clinical decision-making. This paper proposes a real-time symmetric Independent Component Analysis (ICA)-based Code Division Multiple Access (CDMA) receiver, termed symmetric independent CDMA (i-CDMA) receiver, to enhance multiuser detection in dense healthcare IoT networks. Unlike conventional ICA-CDMA receivers that utilize the orthogonality of users' spreading codes and higher-order statistics (HoS) of users' message symbols in signal separation, the proposed approach utilizes a symmetric independence process that jointly utilizes the independence of separating codes and the statistical independence of users' message symbols. The proposed receiver avoids the runtime optimization of scaling parameter for different user densities and SNR conditions. Furthermore, a modified whitening transform is introduced to exploit the complete signal-plus-noise subspace while avoiding overlearning. Simulation results demonstrate significant performance improvement over symmetric Gram-Schmidt orthogonalization-based standard ICA-CDMA receiver in terms of bit error rate (BER) under varying SNR and user-density scenarios for both uplink and downlink systems. The proposed receiver provides a scalable and interference-resilient communication framework suitable for real-time IoT-enabled healthcare monitoring applications.

Indexed as

Internet of ThingsAlgorithmsDigital HealthHumansMonitoring, PhysiologicRemote Patient MonitoringSignal Processing, Computer-AssistedHigher-order StatisticsIndependent Component AnalysisIoT-enabled healthcaremultiuser interference mitigationreal-time multiuser detectionremote patient monitoringwearable medical sensors

Identifiers

PMID42515383
PMCPMC13418916

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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.