Data-Driven-Based Relay Selection and Cooperative Beamforming for Non-Regenerative Multi-Antenna Relay Networks

• Published in: IEEE access 2021, Vol.9, p.167154-167161
• Main Authors: Luo, Jie, Zhao, Sai, Yang, Zhao, Huang, Gaofei, Tang, Dong
• Format: Article
• Language: English
• Subjects: Algorithms; Antennas; Array signal processing; Beamforming; Classification; Continuity (mathematics); Feature extraction; Non-regenerative; Optimization; Relay networks; Relay networks (telecommunication); relay selection; Relays; sample imbalance; support vector machine (SVM); Support vector machines; Training; Wireless sensor networks
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3136332

In this paper, an attempt to exploit the benefits of data-driven methods in solving joint relay selection and beamforming for non-regenerative relay networks has been made. The common relay selection and beamforming optimization problem aiming to maximize the receiver's achievable rate under the constraint of relay transmit power is intrinsically hard since the mixed discrete and continuous variables. The direct map from channel state information to select a relay with optimized beamforming weights via data-driven methods often fails to yield good results. To overcome this difficulty, we propose a two-stage algorithm based on data-driven method. Firstly, we convert relay selection to a multi-class classification problem, and a support vector machine (SVM) based data-driven scheme is proposed to determine the best relay. After the relay is selected, we utilize a closed-form solution to obtain the corresponding relay beamforming weights. Since the number of relays is often more than two, the sample imbalance problem exists in the classification problem, considered in our proposed data-driven scheme. The core idea of SVM based classification method is to train the optimal parameters of the SVM classifier through a large number of offline sample data. In this way, the computation of relay selection can be transferred to offline SVM training. Simulation results demonstrate that the performance of the proposed method is close to that of the global optimal relay selection scheme. Moreover, our proposed scheme has much lower complexities than the global optimal relay selection scheme, especially when the number of relays is large.