A digital signal processing architecture for soft-output MIMO lattice reduction aided detection

Many wireless communication standards now include the use of multiple transmit and receive antennas as a means of achieving increased throughput or spectral efficiency, including LTE, WiMAX and WiFi (IEEE 802.11n). The task of a detector for a multi-input multi-output (MIMO) communications channel is to separate the spatially mixed and noise-corrupted data streams, and to produce reliable estimates of the transmitted bits. The brute-force maximum-likelihood (ML) detector provides optimal error-rate performance, but is computationally infeasible when either dense symbol constellations or large numbers of antennas are used. Hardware implementation of ML receivers is therefore very challenging, leading to linear detectors based on well-known approaches such as zero forcing (ZF) or minimum mean-square error (MMSE) detection, or nonlinear methods such as successive interference cancellation (SIC), which offer manageable receiver complexity at the expense of highly suboptimal error-rate performance.