Deep Learning for Enhancing Efficiency and Optimization in Modern Communication Systems

Deep learning technology transforms network communications by delivering innovative solutions to handle modern network problems. This report studies deep learning technology use within communication systems by presenting its benefits for channel coding and decoding plus channel estimation and resource allocation. The research evaluates how deep learning improves communication operations with better results while using resources more efficiently than traditional systems. Our research shows how deep learning works better than traditional methods in dynamic networks and we analyze its use in current 5G systems. Our survey examines the different neural network types including CNNs, RNNs, and reinforcement learning alongside their applications in DL. Despite these advances deep learning technologies face difficulties with processing power requirements and data availability while also needing more secure protection. The research provides suggestions for future work by showing how to merge deep learning with edge and fog computing platforms while also using quantum computing and privacy protection methods. Deep learning technology will keep getting better to create next-level network systems that think smarter and work better.