Complexity analysis of Lattice Reduction aided increasing Radius algorithm in MIMO systems

The applications of wireless communication may be modelled as integer least square (ILS) problem. Finding the solution to this problem is to find the closest lattice point to a given point. Sphere Decoding (SD) is applied to search the closest lattice point. SD is used in Multiple Input Multiple Output (MIMO) systems to achieve the performance equivalent to Maximum Likelihood (ML) decoding with reduced complexity. One of the issues in the SD is selection of the initial search radius of the hypersphere. In this paper, it is proposed to select the initial search radius from the Look up Table (LUT) which is generated using Radius Choice Algorithm. The radius is updated using the increasing radius algorithm. Some of the pre processing methods such as Lattice Reduction (LR) may be applied before sphere decoding, to convert ILS problem into simple. Lattice reduction is a powerful combdos enq rnkuhmf chudqrd oqnakdl r hmunkuhmf onhms k`sshbdrKdmrsq`+ Kdmrsq` `mc Knu` ́sz (LLL) is a strategic approach to lattice reduction. The technique reduces the complexity of the SD by searching through less number of paths. A combination of radius selection, radius updating along with LR, reduces complexity significantly. The search process begins with an initial radius from LUT and updates the radius using Increasing Radius algorithm. The simulations reveal that the LR aided decoding reduces the number of visited nodes in the search process of SD. The initial search radius reduced by 30% over the small SNR range i.e. from 1 to 5dB, in turn reduces the average number of Floating Point Operations (FLOPS) by 45%, without degrading performance.