Deep Learning based Intelligent Spectrum Sensing Framework Optimizing Dynamic Radio Resource Allocation

Efficient spectrum utilization in today's wireless communications requires intelligent spectrum sensing because dynamic spectrum access depends on it for resource allocation. This study presents a new deep learning framework for spectrum sensing which uses convolutional neural networks together with long short-term memory networks. Decision-making processes in real-time employ hybrid architecture which analyses both spectrum data spatial patterns as well as its temporal evolution through reinforcement learning mechanisms. The spectrum sensing framework using deep learning achieved 97.8% accuracy in detecting spectrum holes while reaching 95.3% precision in identifying primary users through its implementation which resulted in a 42% better spectrum utilization than conventional energy detection methods. Under -20dB to 20dB SNR conditions the system maintained steady performance that generated false alarms less than 0.03 times per observation. The proposed system design provides improved spectrum detection capabilities and resource distribution capabilities which makes it applicable for on-going wireless networks in congested urban spaces.