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

Weihua WU; Guanhua CHAI; Qinghai YANG; Runzi LIU

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

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

Topics: Mobile AI | 更新时间：2024-06-05

- Deep and robust resource allocation for random access network based with imperfect CSI
- Journal on Communications Vol. 41, Issue 7, Pages: 29-37(2020)
- 作者机构：
  
  1. 西安电子科技大学通信工程学院，陕西西安 710071
  2. 西安建筑科技大学信息与控制工程学院，陕西西安 710055
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-436x.2020148
  CLC： TP393
- Online First：2020-07，
  
  Published：25 July 2020
- 稿件说明：
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Weihua WU, Guanhua CHAI, Qinghai YANG, et al. Deep and robust resource allocation for random access network based with imperfect CSI[J]. Journal on Communications, 2020, 41(7): 29-37.
DOI：

Weihua WU, Guanhua CHAI, Qinghai YANG, et al. Deep and robust resource allocation for random access network based with imperfect CSI[J]. Journal on Communications, 2020, 41(7): 29-37. DOI： 10.11959/j.issn.1000-436x.2020148.

摘要

针对无线随机接入网络中通信信道状态信息（C-CSI）和干扰信道状态信息（I-CSI）均不确定的情况，提出了一种深度稳健资源分配架构。该资源分配架构将无线网络的资源优化目标看作一个学习问题，利用深度神经网络（DNN）以无监督的方式学习最优资源分配策略。通过将不确定的CSI建模为椭圆形状的不确定性集合，提出了一个由2个DNN级联构成的网络结构，第一个是不确定的CSI处理单元，第二个是功率控制单元。然后，提出了一种交替迭代训练算法用于联合训练2个级联的DNN单元。最后，仿真比较了稳健学习策略和非稳健学习策略下的网络性能，验证了所提算法的有效性。

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.

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references

HUANG Y , CHEN Y , HOU Y T , et al . Recent advances of LTE/Wi-Fi coexistence in unlicensed spectrum [J ] . IEEE Network , 2018 , 32 ( 2 ): 107 - 113 .

ZHENG K , ZHENG Q , CHATZIMISIOS P , et al . Heterogeneous vehicular networking:a survey on architecture,challenges,and solutions [J ] . IEEE Communications Surveys ＆ Tutorials , 2015 , 17 ( 4 ): 2377 - 2396 .

钱志鸿 , 王雪 . 面向5G通信网的D2D技术综述 [J ] . 通信学报 , 2016 , 37 ( 7 ): 1 - 14 .

QIAN Z H , WANG X . Reviews of D2D technology for 5G communication networks [J ] . Journal on Communications , 2016 , 37 ( 7 ): 1 - 14 .

WU W , YANG Q , LIU R , et al . Protocol design and resource allocation for LTE-U system utilizing licensed and unlicensed bands [J ] . IEEE Access , 2019 , 7 : 67068 - 67080 .

WU W , YANG Q , LIU R , et al . Online spectrum partitioning for LTE-U and WLAN coexistence in unlicensed spectrum [J ] . IEEE Transactions on Communication , 2020 , 68 ( 1 ): 506 - 520 .

YU W , GINIS G , CIOFFIFI J M . Distributed multiuser power control for digital subscriber lines [J ] . IEEE Journal on Selected Areas in Communications , 2002 , 20 ( 5 ): 1105 - 1115 .

SHI Q , RAZAVIYAYN M , LUO Z Q , et al . An iteratively weighted MMSE approach to distributed sum-utility maximization for a MIMO interfering broadcast channel [J ] . IEEE Transactions on Signal Processing , 2011 , 59 ( 9 ): 4331 - 4340 .

SUN H , CHEN X,SHIQ , et al . Learning to optimize:training deep neural networks for wireless resource management [C ] // 2017 IEEE 18th International Workshop on Signal Processing Advances in Wireless Communications . Piscataway:IEEE Press , 2017 : 1 - 6 .

HEE W , CHO D H , KIM M . Resource allocation for multi-channel underlay cognitive radio network based on deep neural network [J ] . IEEE Communication Letter , 2018 , 22 ( 1 ): 1942 - 1945 .

DMER S , HOYDIS C J , BRINK S T . Deep learning based communication over the air [J ] . IEEE Journal of Selected Topics in Signal Processing , 2018 , 12 ( 1 ): 132 - 143 .

EISEN M , ZHANG C L , CHAMMON F O , et al . Learning optimal resource allocations in wireless systems [J ] . IEEE Transactions on Signal Processing , 2019 , 67 ( 10 ): 2775 - 2790 .

LEE H , LEE S H , QUEK T Q . Deep learning for distributed optimization:applications to wireless resource management [J ] . IEEE Journal on Selected Areas in Communications , 2019 ,( PP ):1.

廖晓闽 , 严少虎 , 石嘉 , 等 . 基于深度强化学习的蜂窝网资源分配算法 [J ] . 通信学报 , 2019 , 40 ( 2 ): 11 - 18 .

LIAO X M , YAN S H , SHI J , et al . Deep reinforcement learning based resource allocation algorithm in cellular networks [J ] . Journal on Communications , 2019 , 40 ( 2 ): 11 - 18 .

LEE W , KIM M , CHO D . Transmit power control using deep neural network for underlay device-to-device communication [J ] . IEEE Communications Letters , 2019 , 8 ( 1 ): 141 - 144 .

KERRET P , GESBERT D , RILIPPONE M . Team deep neural networks for interference channels [C ] // 2018 IEEE International Conference on Communications Workshops . Piscataway:IEEE Press , 2018 : 1 - 6 .

AI-KHASIB T , SHENOUDA M B , LAMPE L . Dynamic spectrum management for multiple-antenna cognitive radio systems:Designs with imperfect CSI [J ] . IEEE Transactions on Wireless Communications , 2011 , 10 ( 9 ): 2850 - 2859 .

MPLARI N , PARSAEEFARD S , AZMI P , et al . Robust ergodic uplink resource allocation in underlay OFDMA cognitive radio networks [J ] . IEEE Transactions on Mobile Computing , 2016 , 15 ( 2 ): 419 - 431 .

PARSAEEFATD S , SHARAFAT A R . Robust worst-case interference control in underlay cognitive radio networks [J ] . IEEE Transactions on Vehicular Technology , 2012 , 61 ( 8 ): 3731 - 3745 .

SAGARI S , BAYSTING S , SAHA D , et al . Coordinated dynamic spectrum management of LTE-U and Wi-Fi networks [C ] // 2015 IEEE International Symposium on Dynamic Spectrum Access Networks . Piscataway:IEEE Press , 2015 : 1 - 12 .

LIEW S C , KAI C H , LEUNG H C , et al . Back-of-the-envelope computation of throughput distributions in CSMA wireless networks [J ] . IEEE Transactions on Mobile Computing , 2010 , 9 ( 9 ): 1319 - 1331 .

MALLICK S , DEVARAJAN R , LOODDRICHEH R A , et al . Robust resource optimization for cooperative cognitive radio networks with imperfect CSI [J ] . IEEE Transactions on Wireless Communications , 2014 , 14 ( 2 ): 907 - 920 .

SUN H , CHEN X , SHI Q , et al . Learning to optimize:Training deep neural networks for interference management [J ] . IEEE Transactions Signal Process , 2018 , 66 ( 20 ): 5438 - 5453 .

RIBEIRO A . Optimal resource allocation in wireless communication and networking [J ] . EURASIP Journal on Wireless Communications and Networking , 2012 , 2012 ( 1 ): 272 - 285 .

YIN R , YU G , MAAREF A , et al . A framework for co-channel interference and collision probability tradeoff in LTE licensed-assisted access networks [J ] . IEEE Transactions on Wireless Communications , 2016 , 15 ( 9 ): 6078 - 6090 .

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