Effective reduction for sphere decoder in linear multi-input multi-output channel systems

Abstract

In linear multi-input multi-output channel systems, reduction process is employed to reduce the computational cost of sphere decoding (SD) algorithm. Usually a reduction process includes not only permutations but also unimodular transformations. However, owing to the box-constraint, the current reduction strategies are limited to only permutations, which makes SD algorithm still very time-consuming. In this study a theoretical complexity analysis on SD algorithm is first proposed to show what kind of criteria a reduction process should pursue. Then a new reduction strategy which combines permutations with unimodular transformations is presented to obtain a better reduced detection problem. Simulation results showed that this new reduction strategy can make SD algorithm much more efficient than those reduction strategies with only permutations.

Sphere Decoding in Parallel Mode and its Performance

“…Among them, Sphere decoding is a very powerful algorithm to find the optimal solution in many scenarios [4][5][6][7][8][9][10][11][12]. Many works have been done to promote this algorithm [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. To utilize the benefit of parallel calculation in signal process is a good method to promote the performance [30][31].…”

Cooperative transmission schemes for energy‐efficient collaborative wireless sensor networks

“…Channel adaptive processing intelligence schemes such as lattice reduction (LR), sphere decoding etc. have been proposed in the literature [16][17][18] for MIMO systems to help the receiver to perform near optimal data detection. Optimal maximum likelihood detection with sphere decoding can achieve full diversity, but less complex suboptimal detectors with LR perform close to optimal and have the potential to achieve full diversity [19].…”