Intelligent Channel Estimation and Equalization in Cognitive Networks using Deep Learning Techniques
Keywords:
Cognitive Radio Networks, Channel Estimation, Equalization, Deep Learning, Neural Networks, Dynamic Spectrum Access, Bit Error Rate, Intelligent Wireless CommunicationAbstract
Cognitive radio networks enable efficient spectrum utilization by dynamically adapting to changing wireless environments. However, accurate channel estimation and effective equalization remain major challenges due to spectrum mobility, interference, and rapidly varying channel conditions. Conventional estimation and equalization techniques are often limited by linear assumptions and predefined channel models, leading to performance degradation during handover scenarios. This research proposes an intelligent framework that employs deep learning techniques for channel estimation and equalization in cognitive networks. Deep neural network models are trained to learn complex channel characteristics and compensate for noise, fading, and interference effects. The proposed approach aims to enhance estimation accuracy, reduce bit error rate, and improve overall system reliability. Simulation results are expected to demonstrate superior performance of the deep learning-based approach compared to traditional methods, making it suitable for next-generation cognitive and intelligent wireless communication systems.
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