Efficient Transmission of Radiomaps via Physics-Enhanced Semantic Communications
Yueling Zhou, Achintha Wijesinghe, Yue Wang, Songyang Zhang, Zhipeng Cai

TL;DR
This paper introduces a physics-enhanced semantic communication framework for efficient radiomap transmission that leverages semantic compression and generative models to reduce bandwidth while maintaining high accuracy in dynamic wireless environments.
Contribution
It proposes a novel semantic communication approach using Radio Depth Maps and generative adversarial networks for efficient, real-time radiomap transmission in wireless networks.
Findings
Achieves high accuracy in radio coverage recovery
Reduces communication overhead significantly
Effective in multi-user edge computing scenarios
Abstract
Enriching information of spectrum coverage, radiomap plays an important role in many wireless communication applications, such as resource allocation and network optimization. To enable real-time, distributed spectrum management, particularly in the scenarios with unstable and dynamic environments, the efficient transmission of spectrum coverage information for radiomaps from edge devices to the central server emerges as a critical problem. In this work, we propose an innovative physics-enhanced semantic communication framework tailored for efficient radiomap transmission based on generative learning models. Specifically, instead of bit-wise message passing, we only transmit the key "semantics" in radiomaps characterized by the radio propagation behavior and surrounding environments, where semantic compression schemes are utilized to reduce the communication overhead. Incorporating the…
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Taxonomy
TopicsEnergy Harvesting in Wireless Networks · Molecular Communication and Nanonetworks · Energy Efficient Wireless Sensor Networks
