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@article{204074,
        author = {Shruthi N and K Ramesha and Leelavathi H P},
        title = {Compressed Sensing Based Approach for Sparse Signal Reconstruction and User Activity Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {1426-1432},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204074},
        abstract = {The escalating demand for wireless connectivity and the proliferation of Internet of Things (IoT) devices have driven the exploration of new techniques to enhance the capacity and efficiency of wireless communication systems. In this context, the integration of grant-free uplink Non-Orthogonal Multiple Access (NOMA) with compressed sensing for active user detection has seen increased prominence. This paper presents a comprehensive overview of this emerging approach. By leveraging the principles of NOMA, multiple users can efficiently share the same time-frequency resources, improving spectral efficiency. Compressed sensing techniques are used to detect active users by capitalizing on the sparse nature of user activity. This reduces computational complexity along with increasing the real-time processing capabilities, especially in scenarios with high user density. The synergy between grant-free uplink NOMA and compressed sensing holds promise for addressing the challenges of modern wireless communication systems, including IoT networks and smart cities. Utilizing this idea, we introduced sparsity induced compressed sensing mechanism for activity detection, and symbol reconstruction. This approach is found to achieve enhanced performance in parameters - probability of detection, NMSE, and symbol error rate as shown by experimental analysis.},
        keywords = {Grant free NOMA, Compressed sensing, activity detection, sparsity.},
        month = {June},
        }