Collaborative Relay Beamforming Strategies for Multiple Destinations with Guaranteed QoS in Wireless Machine-to-Machine Networks

Abstract

The Machine-to-Machine (M2M) communications allow information exchange between machine devices, which can be carried out without any human interaction. However, there are a large number of small and low-power machine devices in the wireless M2M networks. To guarantee the quality of service (QoS) requirements of the destination devices, we study the amplify-and-forward (AF) relay beamforming, where multiple relay M2M devices can transmit signals from the source M2M device to multiple destination M2M devices. In this paper, we propose two iterative strategies to jointly optimize the source antenna selection and the collaborative relay beamforming weights with the aid of perfect channel state information (CSI). The aim of the proposed strategies is to maximize the worst-case received signal-to-interference-and-noise ratio (SINR) under two different types of relay power constraints, which are the total relay power constraint and individual relay power constraints, respectively. Using the semidefinite relaxation (SDR) technique, the optimization problem of collaborative relay beamforming can be formulated as a semidefinite programming (SDP) problem, which can be optimally solved. Simulation results validate our theoretical analysis and demonstrate that after several iterations, the performance of the proposed iterative strategies can obtain near-optimal performance.