1 research outputs found
Performance / Complexity Trade-offs of the Sphere Decoder Algorithm for Massive MIMO Systems
Massive MIMO systems are seen by many researchers as a paramount technology
toward next generation networks. This technology consists of hundreds of
antennas that are capable of sending and receiving simultaneously a huge amount
of data. One of the main challenges when using this technology is the necessity
of an efficient decoding framework. The latter must guarantee both a low
complexity and a good signal detection accuracy. The Sphere Decoder (SD)
algorithm represents one of the promising decoding algorithms in terms of
detection accuracy. However, it is inefficient for dealing with large MIMO
systems due to its prohibitive complexity. To overcome this drawback, we
propose to revisit the sequential SD algorithm and implement several variants
that aim at finding appropriate trade-offs between complexity and performance.
Then, we propose an efficient high-level parallel SD scheme based on the
master/worker paradigm, which permits multiple SD instances to simultaneously
explore the search space, while mitigating the overheads from load imbalance.
The results of our parallel SD implementation outperform the state-of-the-art
by more than 5x using similar MIMO configuration systems, and show a
super-linear speedup on multicore platforms. Moreover, this paper presents a
new hybrid implementation that combines the strengths of SD and K-best
algorithms, i.e., maintaining the detection accuracy of SD, while reducing the
complexity using the K-best way of pruning search space. The hybrid approach
extends our parallel SD implementation: the master contains the SD search tree,
and the workers use the K-best algorithm to accelerate its exploration. The
resulting hybrid approach enhances the diversification gain, and therefore,
lowers the overall complexity. Our synergistic hybrid approach permits to deal
with large MIMO configurations up to 100x100, without sacrificing the accuracy
and complexity