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.

Performance of cooperative multi-hop multiuser relaying networks

This paper presents both capacity and error performances of cooperative multi-hop multiuser relay system. All multi-hop cooperative relays in the system apply maximal-ratio combining (MRC) scheme, while signal transmission to end users is carried out using opportunistic scheduling algorithm. Numerical results for spectral efficiency and average symbol error probability (ASEP) for considered system are presented and discussed.