How Much Data is Needed for Channel Knowledge Map Construction? (2312.06966v1)
Abstract: Channel knowledge map (CKM) has been recently proposed to enable environment-aware communications by utilizing historical or simulation generated wireless channel data. This paper studies the construction of one particular type of CKM, namely channel gain map (CGM), by using a finite number of measurements or simulation-generated data, with model-based spatial channel prediction. We try to answer the following question: How much data is sufficient for CKM construction? To this end, we first derive the average mean square error (AMSE) of the channel gain prediction as a function of the sample density of data collection for offline CGM construction, as well as the number of data points used for online spatial channel gain prediction. To model the spatial variation of the wireless environment even within each cell, we divide the CGM into subregions and estimate the channel parameters from the local data within each subregion. The parameter estimation error and the channel prediction error based on estimated channel parameters are derived as functions of the number of data points within the subregion. The analytical results provide useful guide for CGM construction and utilization by determining the required spatial sample density for offline data collection and number of data points to be used for online channel prediction, so that the desired level of channel prediction accuracy is guaranteed.
- available online at https://arxiv.org/abs/2309.07460.
- Y. Zeng and X. Xu, “Toward environment-aware 6G communications via channel knowledge map,” IEEE Wireless Commun., vol. 28, pp. 84–91, Jun. 2021.
- D. Wu, Y. Zeng, S. Jin, and R. Zhang, “Environment-aware hybrid beamforming by leveraging channel knowledge map,” IEEE Trans. on Wireless Commun., 2023. Early Access.
- 3GPP TR 36.873, “Study on 3D channel model for LTE,” tech. rep., 2017.
- M. Malmirchegini and Y. Mostofi, “On the spatial predictability of communication channels,” IEEE Trans. on Wireless Commun., vol. 11, pp. 964–978, Mar. 2012.
- J. Thrane, D. Zibar, and H. L. Christiansen, “Model-aided deep learning method for path loss prediction in mobile communication systems at 2.6 GHz,” IEEE Access, vol. 8, pp. 7925–7936, 2020.
- W. Liu and J. Chen, “UAV-aided radio map construction exploiting environment semantics,” IEEE Trans. Wireless Commun., vol. 22, no. 9, pp. 6341–6355, 2023.
- V.-P. Chowdappa, C. Botella, and J. J. Samper-Zapater, “Distributed radio map reconstruction for 5G automotive,” IEEE Intelligent Transportation Systems Magazine, vol. 10, 2018.
- K. Sato and T. Fujii, “Kriging-based interference power constraint: Integrated design of the radio environment map and transmission power,” IEEE Trans. Cogn. Commun. Netw., vol. 3, no. 1, pp. 13–25, 2017.
- A. Verdin, C. Funk, B. Rajagopalan, and W. Kleiber, “Kriging and local polynomial methods for blending satellite-derived and gauge precipitation estimates to support hydrologic early warning systems,” IEEE Trans. Geosci. Remote Sens., vol. 54, no. 5, pp. 2552–2562, 2016.
- K. Li, P. Li, Y. Zeng, and J. Xu, “Channel knowledge map for environment-aware communications: EM algorithm for map construction,” in Proc. IEEE Wireless Commun. and Networking Conf., 2022.
- M. Gudmundson, “Correlation model for shadow fading in mobile radio systems,” Electron. Lett., vol. 27, Nov. 1991.
- D. S. Kalogerias and A. P. Petropulu, “Nonlinear SpatioTemporal channel gain map tracking in mobile cooperative networks,” in Proc. IEEE SPAWC, 2015.
- Q. Liao, S. Valentin, and S. Stanczak, “Channel gain prediction in wireless networks based on spatial-temporal correlation,” in Proc. IEEE SPAWC, 2015.
- L. S. Muppirisetty, T. Svensson, and H. Wymeersch, “Spatial wireless channel prediction under location uncertainty,” IEEE Trans. Wireless Commun., vol. 15, no. 2, pp. 1031–1044, 2016.
- Wiley-Interscience, 2015.
- available online at https://doi.org/10.5281/zenodo.7839447.
- E. Krijestorac, S. Hanna, and D. Cabric, “Spatial signal strength prediction using 3D maps and deep learning,” in Proc. IEEE Inter. Confer. Commun., 2021.
- A. Karttunen, A. F. Molisch, S. Hur, J. Park, and C. J. Zhang, “Spatially consistent street-by-street path loss model for 28-GHz channels in micro cell urban environment,” IEEE Trans. Wireless Commun., vol. 16, pp. 7538–7550, Sep. 2017.
- John Wiley & Sons, Ltd, 3 ed., 2013.
- N. Cressie, “Fitting variogram models by weighted least squares,” J. Int. Assoc. Math. Geol., vol. 7, 1985.
- Elsevier Academic Press, 7 ed., 2007.