Adaptive Semantic Communications for Time-Critical 6G Wireless Networks

In the rapidly evolving landscape of 6G wireless networks, time-sensitive applications demand efficient data transmission that prioritizes meaningful content over raw signals. This paper introduces a novel adaptive semantic communication system designed for ultra-reliable and low-latency applications, such as autonomous vehicles, drone monitoring, and satellite imaging. The system uses a multi-exit autoencoder to dynamically alter encoding depth based on input data complexity, maximizing the balance of semantic accuracy and processing performance. A lightweight contextual multi-armed bandit (CMAB) mechanism guides exit selection, leveraging a hybrid feature extractor that combines an encoder-derived semantic features vector with statistical image metrics to enable real-time decisions without supervision. The design of this adaptable architecture, an online learning policy for resource allocation, and validation through tests with object detection benchmarks are all significant contributions. Results demonstrate superior performance over traditional and neural-based codecs, particularly under high compression ratios, with enhanced fidelity in latency-constrained environments. This approach paves the way for practical 6G deployments in edge-intelligent systems, offering reduced overhead while preserving critical information for upstream and downstream tasks.