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

Area of Science:

• Biomedical Engineering
• Signal Processing
• Wireless Communications

Background:

• Internet of Things (IoT) healthcare systems utilize wearable sensors and remote monitoring devices.
• Dense communication scenarios with numerous devices transmitting physiological data lead to multiuser interference, degrading reliability and increasing error rates.
• This interference can compromise clinical decision-making in healthcare IoT applications.

Purpose of the Study:

• To propose a real-time symmetric Independent Component Analysis (ICA)-based Code Division Multiple Access (CDMA) receiver, termed symmetric independent CDMA (i-CDMA), for enhanced multiuser detection in dense healthcare IoT networks.
• To improve the reliability and accuracy of data transmission in healthcare IoT environments.

Main Methods:

• The proposed symmetric i-CDMA receiver utilizes a symmetric independence process, jointly leveraging the independence of separating codes and the statistical independence of users' message symbols.
• It avoids runtime optimization of scaling parameters for varying user densities and SNR conditions.
• A modified whitening transform is introduced to exploit the complete signal-plus-noise subspace while preventing overlearning.

Main Results:

• Simulation results show significant performance improvement over standard ICA-CDMA receivers, particularly in terms of bit error rate (BER).
• The proposed receiver demonstrates superior performance under varying signal-to-noise ratio (SNR) and user-density scenarios for both uplink and downlink systems.
• It offers enhanced reliability and reduced error rates compared to conventional methods.

Conclusions:

• The symmetric i-CDMA receiver provides a scalable and interference-resilient communication framework for real-time IoT-enabled healthcare monitoring.
• This approach enhances the robustness of wireless communication in dense healthcare IoT networks.
• It contributes to more reliable clinical decision-making by ensuring data integrity.