Optimized Configurable Architectures for Scalable Soft-Input Soft-Output MIMO Detectors with 256-QAM

摘要

This paper presents an optimized low-complexity and high-throughputmultiple-input multiple-output (MIMO) signal detector core for detectingspatially-multiplexed data streams. The core architecture supports variouslayer configurations up to 4, while achieving near-optimal performance, as wellas configurable modulation constellations up to 256-QAM on each layer. The coreis capable of operating as a soft-input soft-output log-likelihood ratio (LLR)MIMO detector which can be used in the context of iterative detection anddecoding. High area-efficiency is achieved via algorithmic and architecturaloptimizations performed at two levels. First, distance computations and slicingoperations for an optimal 2-layer maximum a posteriori (MAP) MIMO detector areoptimized to eliminate the use of multipliers and reduce the overhead ofslicing in the presence of soft-input LLRs. We show that distances can beeasily computed using elementary addition operations, while optimal slicing isdone via efficient comparisons with soft decision boundaries, resulting in asimple feed-forward pipelined architecture. Second, to support more layers, anefficient channel decomposition scheme is presented that reduces the detectionof multiple layers into multiple 2-layer detection subproblems, which map ontothe 2-layer core with a slight modification using a distance accumulation stageand a post-LLR processing stage. Various architectures are accordinglydeveloped to achieve a desired detection throughput and run-timereconfigurability by time-multiplexing of one or more component cores. Theproposed core is applied as well to design an optimal multi-user MIMO detectorfor LTE. The core occupies an area of 1.58MGE and achieves a throughput of 733Mbps for 256-QAM when synthesized in 90 nm CMOS.