Channel Assignment in Uplink Wireless Communication Using Machine Learning Approach
作者： Guangyu Jia , Zhaohui Yang , Hak-Keung Lam , Jianfeng Shi , Mohammad Shikh-Bahaei
DOI: 10.1109/LCOMM.2020.2968902
关键词: Computational complexity theory 、 Convex optimization 、 Machine learning 、 Artificial intelligence 、 Convolutional neural network 、 Wireless 、 Artificial neural network 、 Deep learning 、 Communication channel 、 Random forest 、 Telecommunications link 、 Computer science
摘要: This letter investigates a channel assignment problem in uplink wireless communication systems. Our goal is to maximize the sum rate of all users subject integer constraints. A convex optimization based algorithm provided obtain optimal assignment, where closed-form solution obtained each step. Due high computational complexity algorithm, machine learning approaches are employed efficient solutions. More specifically, data generated by using and original converted regression which addressed integration convolutional neural networks (CNNs), feed-forward (FNNs), random forest gated recurrent unit (GRUs). The results demonstrate that method largely reduces computation time with slightly compromising prediction accuracy.
sci-hub.se 参考文章(14)
Panos M Pardalos, Convex Optimization Theory ,(2009)
Weifeng Su, Zoltan Safar, K.J. Ray Liu, Space-time signal design for time-correlated Rayleigh fading channels international conference on communications. ,vol. 5, pp. 3175- 3179 ,(2003) , 10.1109/ICC.2003.1204012
Çaglar Gülçehre, Yoshua Bengio, Yoshua Bengio, Yoshua Bengio, KyungHyun Cho, Junyoung Chung, Empirical evaluation of gated recurrent neural networks on sequence modeling arXiv: Neural and Evolutionary Computing. ,(2014)
Martin Längkvist, Lars Karlsson, Amy Loutfi, A review of unsupervised feature learning and deep learning for time-series modeling ☆ Pattern Recognition Letters. ,vol. 42, pp. 11- 24 ,(2014) , 10.1016/J.PATREC.2014.01.008
Ningbo Zhang, Jing Wang, Guixia Kang, Yang Liu, Uplink Nonorthogonal Multiple Access in 5G Systems IEEE Communications Letters. ,vol. 20, pp. 458- 461 ,(2016) , 10.1109/LCOMM.2016.2521374
Stephen Boyd, Lieven Vandenberghe, Convex Optimization Cambridge University Press. ,(2004)
Richard Socher, Nitish Shirish Keskar, Improving Generalization Performance by Switching from Adam to SGD. arXiv: Learning. ,(2017)
Mengyuan Lee, Yuanhao Xiong, Guanding Yu, Geoffrey Ye Li, Deep Neural Networks for Linear Sum Assignment Problems IEEE Wireless Communications Letters. ,vol. 7, pp. 962- 965 ,(2018) , 10.1109/LWC.2018.2843359
Alessio Zappone, Luca Sanguinetti, Merouane Debbah, User Association and Load Balancing for Massive MIMO through Deep Learning 2018 52nd Asilomar Conference on Signals, Systems, and Computers. pp. 1262- 1266 ,(2018) , 10.1109/ACSSC.2018.8645483
Geoffrey Ye Li, Guanding Yu, Mengyuan Lee, Learning to Branch: Accelerating Resource Allocation in Wireless Networks arXiv: Information Theory. ,(2019) , 10.1109/TVT.2019.2953724