Yong Xia

The intensifying demand for data rate and connectivity has resulted in multi-user multiple-input multiple-output (MU-MIMO) deployments. MU-MIMO allows multiple data streams to transmit concurrently in the same spectrum band. These mutually interfering streams need to be processed at the base station (BS), leading to substantial computational complexity requirements. Linear MIMO detectors/precoders are popular due to their relatively low complexity. However, matrix inversion is a challenging task in linear detectors/precoders. Especially in experimental platforms, software-based inversions are infeasible for a large number of users. This work presents Matrix Inversion on Channel Approximation (MICA), a novel method that aims to reduce the complexity of matrix inversion by exploiting the characteristics of channel correlation in the time domain. In low-mobility scenarios (user speeds less than 20km/h), MICA can reduce the average complexity and processing latency required for computing the inverse of 64 × 12 channel matrices by about 90% compared to a conventional scheme, while maintaining almost the same error rate performance.