AI-Based Signal Processing for Efficient Wireless Communication in Industrial IoT Systems

The IIoT systems significantly depend on the effectiveness and reliability of wireless communication to provide real-time monitoring, data collection, and control implementation. Signal processing is one of the primary sources of communication in wireless networks and has created significant steps through the use of artificial intelligence. This paper deliberates how AI-based signal processing techniques improve the performance and robustness of wireless communication within IIoT systems. The main problems are figuring out the channel, reducing noise, allocating resources, and finding bugs. These problems have shown how important AI techniques like deep learning, reinforcement learning, and federated learning are. This paper proposes an effective AI-based signal processing algorithm to enhance the performance of wireless communication in IIoT systems by optimizing throughput, reducing latency, and minimizing energy consumption while ensuring QoS guarantees in dynamic and interference-prone environments.