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Mobile User Environment Detection using Deep Learning based Multi-Output Classification.

, , , , and . WMNC, page 16-23. IEEE, (2019)

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Multi-task Deep Learning based Environment and Mobility Detection for User Behavior Modeling., , , and . WiOpt, page 1-7. IEEE, (2019)Mobile User Environment Detection using Deep Learning based Multi-Output Classification., , , , and . WMNC, page 16-23. IEEE, (2019)Optimization of Unicast Services Transmission for Broadcast Channels in Practical Situations., , and . Bell Labs Tech. J., 17 (1): 5-23 (2012)MPE-IFEC: An enhanced burst error protection for DVB-SH systems., , , , and . Bell Labs Tech. J., 14 (1): 25-40 (2009)Deep Learning Based Speed Profiling for Mobile Users in 5G Cellular Networks., , , and . GLOBECOM, page 1-7. IEEE, (2019)Resilience analysis and quantification method for 5G-Radio Access Networks., , , and . NoF, page 1-9. IEEE, (2022)5G Edge Resource Federation: Dynamic and Cross-domain Network Slice Deployment., , , , , , , and . NetSoft, page 338-340. IEEE, (2018)Constellation shaping for broadcast channels in practical situations., , , and . EUSIPCO, page 96-100. IEEE, (2011)Efficient repair mechanism of real-time broadcast services in hybrid DVB-SH and cellular systems., , , and . Bell Labs Tech. J., 14 (1): 41-54 (2009)Architecture for Real-Time Stream Error Handling in Converged DVB-SH/Cellular Network., , , and . GLOBECOM, page 2870-2874. IEEE, (2008)