  Lattice reduction is a popular preprocessing strategy in multiple-input
multiple-output (MIMO) detection. In a quest for developing a low-complexity
reduction algorithm for large-scale problems, this paper investigates a new
framework called sequential reduction (SR), which aims to reduce the lengths of
all basis vectors. The performance upper bounds of the strongest reduction in
SR are given when the lattice dimension is no larger than 4. The proposed new
framework enables the implementation of a hash-based low-complexity lattice
reduction algorithm, which becomes especially tempting when applied to
large-scale MIMO detection. Simulation results show that, compared to other
reduction algorithms, the hash-based SR algorithm exhibits the lowest
complexity while maintaining comparable error performance.
