面向不确定CSI随机接入网络的深度稳健资源分配

doi:10.11959/j.issn.1000-436x.2020148

Abstract

Abstract:

A deep and robust resource allocation framework was proposed for the random access based wireless networks,where both the communication channel state information (C-CSI) and the interference channel state information (I-CSI) were uncertain.The proposed resource allocation framework considered the optimization objective of wireless networks as a learning problem and employs deep neural network (DNN) to approximate optimal resource allocation policy through unsupervised manner.By modeling the uncertainties of CSI as ellipsoid sets,two concatenated DNN units were proposed,where the first was uncertain CSI processing unit and the second was the power control unit.Then,an alternating iterative training algorithm was developed to jointly train the two concatenated DNN units.Finally,the simulations verify the effectiveness of the proposed robust leaning approach over the nonrobust one.

Key words: deep neural network, random access network, robust optimization, wireless resource allocation

CLC Number:

TP393

Weihua WU,Guanhua CHAI,Qinghai YANG,Runzi LIU. Deep and robust resource allocation for random access network based with imperfect CSI[J]. Journal on Communications, 2020, 41(7): 29-37.

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References 24

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网络参数	数值大小
CSMA链路个数	6
设备之间的距离d	10～100 m随机取值
带宽B/MHz	6
传输效率a	0.6
路径损耗PL	-15.3-50lg(d[m])
高斯白噪声功率谱密度N₀/(dBm·Hz^-1)	-174
干扰阈值Q/dBm	33
最大发射功率P_max/W	10

Deep and robust resource allocation for random access network based with imperfect CSI

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