# Environment Semantics Aided Wireless Communications: A Case Study of mmWave Beam Prediction and Blockage Prediction

arXiv: https://arxiv.org/abs/2301.05837 · HTML: https://mlsanity.com/paper/2301.05837/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2023.

## Built on by (most cited first, 7 of 7)

- [Enhancing Reliability in Federated mmWave Networks: A Practical and Scalable Solution using Radar-Aided Dynamic Blockage Recognition](https://mlsanity.com/paper/2307.06834/) (2023)
- [GSBF: Gaussian Splatting for Environment-Aware Beamforming](https://mlsanity.com/paper/2608.05896/) (2026)
- [Transformer-Based Rate Prediction for Multi-Band Cellular Handsets](https://mlsanity.com/paper/2509.25722/) (2025)
- [QuaMoE-DRF: Proactive Beam and Rate Adaptation via Multimodal Dynamic Radio Map Forecasting in ISAC Networks](https://mlsanity.com/paper/2607.00974/) (2026)
- [Environment-Aware Channel Prediction for Vehicular Communications: A Multimodal Visual Feature Fusion Framework](https://mlsanity.com/paper/2604.02396/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)
- [AIMS: An Agentic AI Framework for Sim-to-Real Multi-Modal ISAC](https://mlsanity.com/paper/2609.39964/) (2026)

## Abstract (arXiv)

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.