Silicon complexity for maximum likelihood MIMO detection using spherical decoding

Multiple-input multiple-output (MIMO) wireless systems increase spectral efficiency by transmitting independent signals on multiple transmit antennas in the same channel bandwidth. The key to using MIMO is in building a receiver that can decorrelate the spatial signatures on the receiver antenna array. Original MIMO detection schemes such as the vertical Bell Labs layered space-time (VBLAST) detector use a ing and cancellation process for detection that is sub-optimal as compared to constrained maximum likelihood (ML) techniques. This paper presents a silicon complexity analysis of ML search techniques for MIMO as applied to the HSDPA extension of UMTS. For MIMO constellations of 4×4 QPSK or lower, it is possible to perform an exhaustive ML search in today´s silicon technologies. When the search complexity exceeds technology limits for high complexity MIMO constellations, it is possible to apply spherical decoding techniques to achieve near-ML performance. The paper presents an architecture for a 4×4 16QAM MIMO spherical decoder with soft outputs that achieves 38.8 Mb/s over a 5-MHz channel using only approximately 10 mm² in a 0.18-μm CMOS process.