Authors: Zezhong Zhang, Guangxu Zhu, Junting Chen, Shuguang Cui

Published on: February 05, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.02729

Summary

• What is new: A novel Cooperative Radio Map Estimation (CRME) approach using a Generative Adversarial Network (GAN) for fast and accurate radio map estimation without needing transmitters’ information.
• Why this is important: The need for quick and precise estimation of radio resource distribution to support various applications in the 6G era.
• What the research proposes: The GAN-CRME approach uses a deep neural network estimator to infer radio maps from distributed received signal strength measurements and geographical maps, enhancing estimation speed and accuracy.
• Results: The proposed GAN-CRME method shows significant capabilities in fast and accurate radio map estimation, including coarse error-correction for inaccurate geographical map information.

Technical Details

Technological frameworks used: Generative Adversarial Network (GAN)

Models used: Deep Neural Network (DNN) estimator

Data used: Distributed Received Signal Strength (RSS) measurements, geographical maps

Potential Impact

Telecommunications industry, 6G technology developers, and wireless communication service providers.

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