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@article{207708,
        author = {Nandini Ramesh Kendre and Dr. Jitesh R. Shinde and Mr. T. A. Mohije and Dr. Ganesh B. Dongare},
        title = {Hardware Acceleration Techniques for Automatic Modulation Classification: A Survey},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {2422-2438},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207708},
        abstract = {AMC is an integral part of present-day wireless communication systems that allow recognizing modulation schemes without any prior information about parameters of the transmitter. As a result of fast expansion of 5G/6G, cognitive radio, software-defined radio, and intelligent spectrum monitoring, the necessity of AMC algorithms with low latency and throughput has been greatly increased. Despite the high classification performance provided by traditional methods based on likelihood, features, and machine learning, the high computational complexity of these approaches has prevented their use in real time on common processing units. Hardware acceleration can be used as a method of overcoming these problems. The aim of this survey is to make a review of hardware acceleration methods for AMC, including CPU-, GPU-, ASIC-, and FPGA-based approaches. Recent developments in machine learning and deep learning-based AMC approaches and their hardware implementation are considered as well. FPGA architectures, AXI4-Stream approaches, hardware–software co-design techniques, and performance metrics are thoroughly analyzed.},
        keywords = {Automatic Modulation Classification (AMC), Hardware Acceleration, Field-Programmable Gate Array (FPGA), Graphics Processing Unit (GPU), Application-Specific Integrated Circuit (ASIC), Machine Learning, Deep Learning, AXI4-Stream, Hardware–Software Co-Design, Wireless Communication, Cognitive Radio, Software-Defined Radio (SDR).},
        month = {August},
        }