An ML-Enhanced Reconfigurable SDR Architecture for Adaptive 5G Wireless Systems

Vijaya Bhaskar, Nagaraju Sancarapu, Vara Prasad Venkata, Kiran Kumar R, Shanmugham Balasundaram

Abstract


This paper presents a machine learning-enhanced Software Defined Radio (SDR) architecture optimized for adaptive 5G wireless communication. The system incorporates predictive ML algorithms to enable real-time modulation selection, FIR filter reconfiguration, and spectrum adaptation based on dynamic channel parameters such as Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), and Received Signal Strength Indicator (RSSI). A Decision Tree Classifier and a Deep Q-Learning agent dynamically determine optimal modulation schemes (BPSK, QPSK, 16-QAM, OQAM) and filter tap configurations (4/8/16 taps), ensuring efficient communication under varying network conditions. Implemented on a Xilinx Zynq SoC using Verilog for datapath design and Python for ML control via AXI4-Lite, the architecture achieves a maximum operating frequency of 182.4 MHz, 40.7% logic utilization, and only 122.3 mW power consumption. Compared to existing SDR implementations, the system demonstrates a 17% frequency improvement, 28% power reduction, and 21% area savings. Real-time ECG transmission confirms the system's adaptability, achieving BER < 10⁻³ at 22 dB SNR and < 10⁻⁵ at 26 dB. These results affirm the viability of the proposed ML-SDR for edge-based biomedical and URLLC applications in 5G networks.

Keywords


Machine Learning; Software Defined Radio; 5G; FIR Filter; Adaptive Modulation; Deep Q-Learning

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References


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DOI: http://doi.org/10.11591/ijict.v15i2.pp699-706

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