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Blind Device Detection via Extended Sparsity Estimation-OMP in Grant-Free NOMA-IoT
Nur Andini1,2, Andriyan Bayu Suksmono1, Joko Suryana1
1School of Electrical Engineering and Informatics, Bandung Institute of Technology (ITB), Bandung 40132, West Java, Indonesia.
Sensors (Basel, Switzerland)
|June 12, 2026
Summary
This study introduces an Extended Sparsity Estimation-Orthogonal Matching Pursuit (ESE-OMP) algorithm for efficient grant-free NOMA-IoT device detection. The ESE-OMP method improves accuracy by not requiring prior knowledge of the number of active devices.
Area of Science:
- Wireless Communications
- Signal Processing
- Internet of Things (IoT)
Background:
- Grant-free Non-Orthogonal Multiple Access (NOMA) systems enable communication without a scheduling process.
- Detecting active devices in grant-free NOMA-IoT systems is challenging due to unknown user numbers.
- Device detection can be framed as a signal reconstruction problem within compressive sensing (CS).
Purpose of the Study:
- To propose a novel algorithm for active device detection in grant-free NOMA-IoT systems.
- To address the challenge of unknown sparsity levels (number of active devices) in device detection.
- To evaluate the performance of the proposed algorithm across different NOMA-IoT system configurations.
Main Methods:
- Development of the Extended Sparsity Estimation-Orthogonal Matching Pursuit (ESE-OMP) algorithm.
- Application of ESE-OMP to both Single Measurement Vector (SMV) and Multiple Measurement Vector (MMV) problems.
- Iterative detection of active devices by monitoring the l1-norm of successive residuals until convergence below a threshold ε.
Main Results:
- The ESE-OMP algorithm successfully detects active devices without prior knowledge of the sparsity level.
- Performance evaluation across irregular Low-Density Spreading-Orthogonal Frequency Division Multiplexing (LDS-OFDM), regular LDS-OFDM, and Pattern Division Multiple Access (PDMA) systems.
- At 10 dB SNR for SMV, regular LDS-OFDM achieved a Bit Error Rate (BER) of 2.95×10-4, outperforming irregular LDS-OFDM (3.78×10-3) and PDMA (1.79×10-2).
- ESE-OMP performance improves as the number of active devices decreases.
Conclusions:
- The proposed ESE-OMP algorithm is an effective method for device detection in grant-free NOMA-IoT systems.
- The algorithm's performance is dependent on the system configuration and the number of active devices.
- ESE-OMP offers a robust solution for scenarios where the number of active users is unknown beforehand.
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