• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

RadioCycle: Deep Dual Learning based Radio Map Estimation

Vol. 16, No. 11, November 30, 2022
10.3837/tiis.2022.11.017, Download Paper (Free):


The estimation of radio map (RM) is a fundamental and critical task for the network planning and optimization performance of mobile communication. In this paper, a RM estimation method is proposed based on a deep dual learning structure. This method can simultaneously and accurately reconstruct the urban building map (UBM) and estimate the RM of the whole cell by only part of the measured reference signal receiving power (RSRP). Our proposed method implements UBM reconstruction task and RM estimation task by constructing a dual U-Net-based structure, which is named RadioCycle. RadioCycle jointly trains two symmetric generators of the dual structure. Further, to solve the problem of interference negative transfer in generators trained jointly for two different tasks, RadioCycle introduces a dynamic weighted averaging method to dynamically balance the learning rate of these two generators in the joint training. Eventually, the experiments demonstrate that on the UBM reconstruction task, RadioCycle achieves an F1 score of 0.950, and on the RM estimation task, RadioCycle achieves a root mean square error of 0.069. Therefore, RadioCycle can estimate both the RM and the UBM in a cell with measured RSRP for only 20% of the whole cell.


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Cite this article

[IEEE Style]
Y. Zheng, T. Zhang, C. Liao, J. Wang and S. Liu, "RadioCycle: Deep Dual Learning based Radio Map Estimation," KSII Transactions on Internet and Information Systems, vol. 16, no. 11, pp. 3780-3797, 2022. DOI: 10.3837/tiis.2022.11.017.

[ACM Style]
Yi Zheng, Tianqian Zhang, Cunyi Liao, Ji Wang, and Shouyin Liu. 2022. RadioCycle: Deep Dual Learning based Radio Map Estimation. KSII Transactions on Internet and Information Systems, 16, 11, (2022), 3780-3797. DOI: 10.3837/tiis.2022.11.017.

[BibTeX Style]
@article{tiis:38011, title="RadioCycle: Deep Dual Learning based Radio Map Estimation", author="Yi Zheng and Tianqian Zhang and Cunyi Liao and Ji Wang and Shouyin Liu and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2022.11.017}, volume={16}, number={11}, year="2022", month={November}, pages={3780-3797}}