Advances in Applications of Artificial Intelligence (AI) in Software-Defined Radio (SDR)
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".
Deadline for manuscript submissions: 10 March 2026 | Viewed by 4
Special Issue Editor
Interests: digital signal processing; wireless communications; machine learning; deep learning; artificial intelligence; education
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Software-Defined Radio (SDR) is a radio communication system where components traditionally implemented in hardware (modulator, demodulator, filters, etc.) are instead implemented by software.
AI enhances SDR by enabling the system to learn, adapt, and make decisions in dynamic radio environments. Benefits include signal denoising, modulation recognition, channel prediction and estimation, and smart spectrum use (cognitive radio), among others.
Recent advances in Software-Defined Radio (SDR) have been driven by a convergence of technologies such as AI, 6G requirements, edge computing, and massive MIMO.
Topics of interest for this Special Issue include, but are not limited to, the following:
- AI methods for modulation classification to identify modulation type (BPSK, QPSK, QAM, etc.);
- Adaptive modulation and coding to dynamically select best modulation/coding based on channel;
- Channel estimation and prediction to estimate or predict the wireless channel state;
- Spectrum sensing and signal detection to detect signal presence in noisy channels;
- Reinforcement learning (RL) for dynamic spectrum access;
- Enhanced cognitive radio systems with real-time spectrum sensing;
- Interference identification;
- Full-duplex and reconfigurable SDRs (transmit and receive simultaneously).
Prof. Dr. Gordana Jovanovic Dolecek
Guest Editor
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Keywords
- machine learning
- deep learning
- wireless communications
- software-defined radio
- modulations
- wireless channel
- 6G communications
- spectrum sensing
- cognitive radio
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