Li, Lixin, Cheng, Qianqian, Tang, Xiao, Bai, Tong, Chen, Wei, Ding, Zhiguo, and Han, Zhu. Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach. Retrieved from https://par.nsf.gov/biblio/10212956. IEEE Transactions on Wireless Communications . Web. doi:10.1109/TWC.2020.3033843.

Li, Lixin, Cheng, Qianqian, Tang, Xiao, Bai, Tong, Chen, Wei, Ding, Zhiguo, & Han, Zhu. Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach. IEEE Transactions on Wireless Communications, (). Retrieved from https://par.nsf.gov/biblio/10212956. https://doi.org/10.1109/TWC.2020.3033843

Li, Lixin, Cheng, Qianqian, Tang, Xiao, Bai, Tong, Chen, Wei, Ding, Zhiguo, and Han, Zhu. "Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach". IEEE Transactions on Wireless Communications (). Country unknown/Code not available. https://doi.org/10.1109/TWC.2020.3033843. https://par.nsf.gov/biblio/10212956.

@article{osti_10212956,
place = {Country unknown/Code not available}, title = {Resource Allocation for NOMA-MEC Systems in Ultra-Dense Networks: A Learning Aided Mean-Field Game Approach}, url = {https://par.nsf.gov/biblio/10212956}, DOI = {10.1109/TWC.2020.3033843}, abstractNote = {}, journal = {IEEE Transactions on Wireless Communications}, author = {Li, Lixin and Cheng, Qianqian and Tang, Xiao and Bai, Tong and Chen, Wei and Ding, Zhiguo and Han, Zhu}, editor = {null} }