Iterative Joint Detection and Channel Estimation Algorithm for Large-scale MIMO System

Abstract

Large-scale Multiple-Input Multiple-Output (MIMO) can promise high spectrum efficiency and low multi-user interference. But the pilot contamination of the uplink channel estimation is the obstacle to acquire the great performance gains. A novel iterative joint channel estimation and detection algorithm is proposed. First, the initial channel information is estimated with the uplink pilots. Then the receiver detects the receiving signals with Match Filter (MF) precoding and Minimum Mean Square Error (MMSE) criterion based on the initial channel estimation. The receiving signal acquires the great large-scale MIMO detection gains and is more lightly interfered than the multiplexed uplink pilots because of the non-coherence of the transmitted data. Thus, it is in turn applied to suppress the pilot contamination in the channel estimation. In the iteration, the updated channel estimation is used for the data detection in the next loop. The theoretical analysis shows that the interference items in the detected signals and the channel estimation are continually decreased with the number of the iteration increasing. At last, the numerical results prove that, the proposed algorithm has significant performance gains comparing with the conventional algorithms. After only one iteration in light pilot contamination cases and three iteration in severe pilot contamination cases, it can obtain the detected data and the channel estimation information with the required performance. The proposed algorithm effectively improves the detection performance and suppress the pilot contamination of the uplink channel estimation, and can be worthy for the large-scale MIMO system.