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Neural Networks in 6G Networks: AI-Native Wireless Evolution
Neural networks (NNs) are poised to be a foundational element of 6G wireless systems, transforming them into "AI-native" architectures. Unlike 5G's focus on enhanced mobile broadband and low-latency IoT, 6G (expected deployment ~2030) aims for terabit-per-second speeds, ultra-reliable sensing-communication integration, and intelligent, self-optimizing networks. NNs enable this by processing vast, dynamic data in real-time, optimizing everything from signal processing to resource allocation. This integration addresses 6G's challenges like massive connectivity, energy efficiency, and security in heterogeneous networks.
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Key Applications of Neural Networks in 5G
Neural networks are integrated across 5G layers—from the physical (PHY) layer to network management—often using architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) models, Graph Neural Networks (GNNs), and Deep Reinforcement Learning (DRL).
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Neural network AI refers to artificial intelligence systems modeled on the human brain's structure and function. These systems, known as artificial neural networks (ANNs), consist of interconnected nodes (neurons) organized in layers that process input data, learn patterns, and generate outputs.
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