Multiple Relay Selection and Beamforming in Dual-Hop Amplify-and-Forward Relay Networks

• Published in: IEEE systems journal Vol. 13; no. 2; pp. 1534 - 1545
• Main Authors: Gouissem, Ala, Samara, Lutfi, Hamila, Ridha, Al-Dhahir, Naofal, Foufou, Sebti
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX"> ell _1</tex-math> </inline-formula>-norm; Algorithms; Array signal processing; Beamforming; Computer Science; eigenvalue decomposition; Eigenvalues; Iterative methods; Networking and Internet Architecture; Optimization; Protocols; Relay networks; Relay networks (telecommunications); Relaying; semidefinite relaxation; Signal to noise ratio; Transmitting antennas; Wireless communication
• ISSN: 1932-8184; 1937-9234; 1937-9234; 1932-8184
• DOI: 10.1109/JSYST.2018.2869853

In this paper, the problems of multiple relay selection and beamforming optimization are considered for a dual-hop amplify-and-forward relaying network under individual and total power constraints. The selection is performed based on maximizing the signal-to-noise ratio with computationally efficient algorithms. For the individual power constraint case, an iterative selection technique based on the use of the squared <inline-formula><tex-math notation="LaTeX">\ell _1</tex-math></inline-formula>-norm is proposed and investigated. We demonstrate that the proposed technique can converge to the same suboptimal solution proposed in the literature with up to <inline-formula><tex-math notation="LaTeX">80\%</tex-math></inline-formula> reduction in the number of iterations. Furthermore, we derive numerical suboptimal beamforming and relay selection solutions based on eigenvalue decomposition for both total and individual power constraints without the need for solving semidefinite programing problems.