  In this paper, we propose an environment semantics aided wireless
communication framework to reduce the transmission latency and improve the
transmission reliability, where semantic information is extracted from
environment image data, selectively encoded based on its task-relevance, and
then fused to make decisions for channel related tasks. As a case study, we
develop an environment semantics aided network architecture for mmWave
communication systems, which is composed of a semantic feature extraction
network, a feature selection algorithm, a task-oriented encoder, and a decision
network. With images taken from street cameras and user's identification
information as the inputs, the environment semantics aided network architecture
is trained to predict the optimal beam index and the blockage state for the
base station. It is seen that without pilot training or the costly beam scans,
the environment semantics aided network architecture can realize extremely
efficient beam prediction and timely blockage prediction, thus meeting
requirements for ultra-reliable and low-latency communications (URLLCs).
Simulation results demonstrate that compared with existing works, the proposed
environment semantics aided network architecture can reduce system overheads
such as storage space and computational cost while achieving satisfactory
prediction accuracy and protecting user privacy.
