Selective Spanning with Fast Enumeration: A Near Maximum-Likelihood MIMO Detector Designed for Parallel Programmable Baseband Architectures

ML and near-ML MIMO detectors have attracted a lot of interest in recent years. However, almost all of the reported implementations are delivered in ASIC or FPGA. Our contribution is to co-optimize the near-ML MIMO detector algorithm and implementation for parallel programmable base-band architectures, such as DSPs with VLIW, SIMD or vector processing features. Although for hardware the architecture can be tuned to fit algorithms, for programmable platforms the algorithm must be elaborately designed to fit the given architecture, so that efficient resource-utilizations can be achieved. By thoroughly analyzing and exploiting the interaction between algorithms and architectures, we propose the SSFE (selective spanning with fast enumeration) as an architecture-friendly near-ML MIMO detector. The SSFE has a distributed and greedy algorithmic structure that brings a completely deterministic and regular dataflow. The SSFE has been evaluated for coded OFDM transmissions over 802.11n channels and 3GPP channels. Under the same performance constraints, the complexity of the SSFE is significantly lower than the K-Best, the most popular detector implemented in hardware. More importantly, SSFE can be easily parallelized and efficiently mapped on programmable baseband architectures. With TI TMS320C6416, the SSFE delivers 37.4 - 125.3 Mbps throughput for 4x4 64 QAM transmissions. To the best of our knowledge, this is the first reported near-ML MIMO detector explicitly designed for parallel programmable architectures and demonstrated on a real-life platform.