Machine Learning-Based Relay Selection for Secure Transmission in Multi-Hop DF Relay Networks

A relay selection method is proposed for physical-layer security in multi-hop decode-and-forward (DF) relaying systems. In the proposed method, cooperative relays are selected to maximize the achievable secrecy rates under DF-relaying constraints by the classification method. Artificial neural networks (ANNs), which are used for machine learning, are applied to classify the set of cooperative relays based on the channel state information of all nodes. Simulation results show that the proposed method can achieve near-optimal performance for an exhaustive search method for all combinations of relay selection, while computation time are reduced significantly. Furthermore, the proposed method outperforms the best relay selection method, in which the best relay in terms of secrecy performance is selected among active ones.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Abbas Javed,et al.  Energy demand prediction through novel random neural network predictor for large non-domestic buildings , 2017, 2017 Annual IEEE International Systems Conference (SysCon).

[3]  Zhi Chen,et al.  Data-Driven-Based Analog Beam Selection for Hybrid Beamforming Under mm-Wave Channels , 2018, IEEE Journal of Selected Topics in Signal Processing.

[4]  Xianbin Wang,et al.  Optimal Relay Selection for Physical-Layer Security in Cooperative Wireless Networks , 2013, IEEE Journal on Selected Areas in Communications.

[5]  Sendhil Mullainathan,et al.  Machine Learning: An Applied Econometric Approach , 2017, Journal of Economic Perspectives.

[6]  Walid Saad,et al.  Proactive Resource Management in LTE-U Systems: A Deep Learning Perspective , 2017, ArXiv.

[7]  Tony Q. S. Quek,et al.  Transmit Antenna Selection in MIMO Wiretap Channels: A Machine Learning Approach , 2018, IEEE Wireless Communications Letters.

[8]  Jong-Ho Lee,et al.  Optimal Power Allocation for Physical Layer Security in Multi-Hop DF Relay Networks , 2016, IEEE Transactions on Wireless Communications.

[9]  Zhu Han,et al.  Improving Wireless Physical Layer Security via Cooperating Relays , 2010, IEEE Transactions on Signal Processing.

[10]  John S. Baras,et al.  Interest, energy and physical-aware coalition formation and resource allocation in smart IoT applications , 2017, 2017 51st Annual Conference on Information Sciences and Systems (CISS).

[11]  Wolfgang Utschick,et al.  Learning the MMSE Channel Estimator , 2017, IEEE Transactions on Signal Processing.

[12]  Di Yuan,et al.  Efficient Minimum-Energy Scheduling with Machine-Learning Based Predictions for Multiuser MISO Systems , 2018, 2018 IEEE International Conference on Communications (ICC).

[13]  Adi Shamir,et al.  RSA Key Extraction via Low-Bandwidth Acoustic Cryptanalysis , 2014, CRYPTO.

[14]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[15]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[16]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[17]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[18]  Symeon Papavassiliou,et al.  Interest-aware energy collection & resource management in machine to machine communications , 2018, Ad Hoc Networks.

[19]  Shi Jin,et al.  Channel Estimation for Massive MIMO Using Gaussian-Mixture Bayesian Learning , 2015, IEEE Transactions on Wireless Communications.

[20]  Jia Zhu,et al.  Joint Cooperative Beamforming and Jamming for Physical-Layer Security of Decode-and-Forward Relay Networks , 2017, IEEE Access.

[21]  Jong-Ho Lee,et al.  Full-Duplex Relay for Enhancing Physical Layer Security in Multi-Hop Relaying Systems , 2015, IEEE Communications Letters.

[22]  Saman Atapattu,et al.  Decentralized Relay Selection in Multi-User Multihop Decode-and-Forward Relay Networks , 2018, IEEE Transactions on Wireless Communications.

[23]  Markus G. Kuhn,et al.  Efficient Stochastic Methods: Profiled Attacks Beyond 8 Bits , 2014, CARDIS.

[24]  Manav R. Bhatnagar,et al.  Performance Analysis of a Path Selection Scheme in Multi-Hop Decode-and-Forward Protocol , 2012, IEEE Communications Letters.

[25]  Hsiao-Chun Wu,et al.  Physical layer security in wireless networks: a tutorial , 2011, IEEE Wireless Communications.

[26]  Euhanna Ghadimi,et al.  A reinforcement learning approach to power control and rate adaptation in cellular networks , 2016, 2017 IEEE International Conference on Communications (ICC).

[27]  Zhigang Jin,et al.  The Physical Layer Security Experiments of Cooperative Communication System with Different Relay Behaviors , 2017, Sensors.

[28]  Abbas Javed,et al.  An Intelligent Real-Time Occupancy Monitoring System Using Single Overhead Camera , 2018, IntelliSys.

[29]  Robert W. Heath,et al.  Adaptation in Convolutionally Coded MIMO-OFDM Wireless Systems Through Supervised Learning and SNR Ordering , 2010, IEEE Transactions on Vehicular Technology.

[30]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[31]  Caijun Zhong,et al.  Opportunistic Relay Selection for Secrecy Enhancement in Cooperative Networks , 2015, IEEE Transactions on Communications.

[32]  Jingon Joung,et al.  Machine Learning-Based Antenna Selection in Wireless Communications , 2016, IEEE Communications Letters.

[33]  Steven P. Weber,et al.  On Cooperative Relaying Schemes for Wireless Physical Layer Security , 2010, IEEE Transactions on Signal Processing.

[34]  Mérouane Debbah,et al.  Online Energy-Efficient Power Control in Wireless Networks by Deep Neural Networks , 2018, 2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC).

[35]  Ranjan K. Mallik,et al.  A Machine Learning Approach for Power Allocation in HetNets Considering QoS , 2018, 2018 IEEE International Conference on Communications (ICC).