Diagonal Amplifying Matrix Design in Multihop Distributed Relay Networks

Abstract

In this paper, we study a multihop distributed relay network consisting of a single source–destination pair and multiple nonregenerative single-antenna relay nodes, which are arranged in different layers. Due to the distributed deployment, the amplifying matrix of the relay nodes at each layer has to be a diagonal matrix. We adopt a layer-by-layer approach to compute the relay amplifying matrices with the objective of maximizing the end-to-end transmission rate. The amplifying matrix of each layer is derived from a per-layer sum-rate maximization problem. When there is only one receiving node at the direct forward layer, the nonconvex per-layer sum-rate maximization problem is equivalently converted into a convex quadratically constrained quadratic program and could be efficiently solved. When there are multiple receiving nodes at the direct forward layer, the nonconvex per-layer sum-rate maximization problem is more challenging. We then transform it into a difference-of-convex program and design iterative convex programing to yield an approximate solution. The analysis shows that the proposed iterative algorithm will converge to local optima of the per-layer sum-rate maximization problem. Simulation results confirm that the proposed scheme outperforms the existing works in terms of the end-to-end transmission rate and is more effective in suppressing noise propagation.