Deep Learning Based Bandwidth

Deep learning is increasingly used to optimize bandwidth utilization in various communication scenarios, primarily addressing the communication bottleneck in distributed training and real-time applications. Current research focuses on adaptive compression techniques, often employing encoder-decoder networks or particle swarm optimization, to dynamically adjust data transmission based on available bandwidth and network conditions. These advancements improve the efficiency of federated learning, bandwidth estimation for video conferencing, and resource allocation in wireless networks like the Internet of Vehicles, ultimately enhancing the performance and scalability of data-intensive applications.