SOAR: Semantic Multi-User MIMO Communications for Reliable Wireless Edge Computing

Ensuring reliability in Mobile Edge Computing Sys- tems (MECS) is challenging due to wireless channel fluctuations, which affect packet delivery rate and real-time task performance. Existing methods, such as packet replication strategies and deep neural network (DNN) splitting, often lack task-specific adaptabil- ity, oversimplifying the effects of channel dynamics in its perfor- mance. To address this issues, we propose Semantic Offloading through Reliability (SOAR), a task-oriented multi-user MIMO framework for wireless edge computing executing vision tasks, e.g. object detection and image classification. SOAR pipeline uses distributional deep reinforcement learning (DDRL) agents with a multi-branched context-aware neural network. Two neural gates analyze onboard features to identify the context and contextual features, enabling a DDRL agent to optimize resource usage and task-specific packet-loss objectives. We evaluated SOAR in real- world vehicular system under line-of-sight (LoS) and non-line- of-sight (NLoS) propagation scenarios, SOAR reduces resource utilization by 35–40% compared to fixed antenna configuration benchmarks.