Compressed channel estimation for MIMO amplify-and-forward relay networks

In this work, we investigate channel estimation problem in Multi-Input Multi-Output (MIMO) cooperative networks that employ the amplify-and-forward (AF) transmission scheme. Least square (LS) and expectation conditional maximization (ECM) have been proposed in the system. However, both of them never take advantage of channel sparsity and then they cause the estimation performance loss. Unlike the linear channel estimation methods, we propose several compressed channel estimation methods to exploit sparsity of the MIMO cooperative channels based on the theory of compressed sensing. At first, we formulate the channel estimation problem as compressed sensing problem by using sparse decomposition theory. Secondly, the lower bound is derived for the estimation and the MIMO relay channel is reconstructed by compressive sampling matching pursuit (CoSaMP) algorithms. Finally, various numerical simulations are given to confirm the superiority of proposed methods than traditional linear channel estimation methods. Simulation results show that our doubly iterative receiver provides an excellent BER performance.