Journal on Communications ›› 2021, Vol. 42 ›› Issue (9): 184-193.doi: 10.11959/j.issn.1000-436x.2021170

• Papers • Previous Articles     Next Articles

DNN-based Sub-6 GHz assisted millimeter wave network power allocation algorithm

Changyin SUN1,2, Liyan LIU1, Fan JIANG1,2, Jing JIANG1,2   

  1. 1 School of Communication and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
    2 Key Laboratory of Information Communication Network and Security, Xi 'an University of Posts and Telecommunications, Xi’an 710121, China
  • Revised:2021-07-01 Online:2021-09-25 Published:2021-09-01
  • Supported by:
    The National Natural Science Foundation of China(61801382);The National Natural Science Foundation of China(61871321);The National Natural Science Foundation of China(62071377);The Natural Science Foundation of Shaanxi Province(2019JZ-06);The Key Chain Project of Shaanxi Province(2020ZDLGY02-06);The Key Chain Project of Shaanxi Province(2019ZDLGY07-06)

Abstract:

Aimed at the problems of the signaling cost and power consumption in the power control measurement of the millimeter wave system, as well as the complexity caused by iteration operations, a millimeter wave link power allocation prediction algorithm using the Sub-6 GHz frequency band was proposed.Firstly, the mapping between the Sub-6 GHz band channel information and the optimal power allocation of the millimeter wave band was analyzed.Then, a deep neural network (DNN) model was utilized to realize this mapping function.To predict the power allocation of millimeter wave channel with Sub-6 GHz channel as input, the neural network was trained with the weighted mean square error minimization method (WMMSE) as the supervisor in different scenarios.The simulation results show that compared with the WMMSE algorithm in millimeter wave band, the proposed algorithm can obtain more than 97% of its sum-rate performance while taking less than 0.1% of the time.

Key words: millimeter wave communication, power allocation, deep learning, neural network

CLC Number: 

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