Significant performance gains are achievable in wireless systems
using a Multi-Input Multi-Output (MIMO) communication system
employing multiple antennas.This architecture is suitable for
higher data rate multimedia communications.One of the challenges in
building a MIMO system is the tremendous processing power required
at the receiver. MIMO Symbol detection involves detecting symbol
from a complex signal at the receiver. Nature Inspired techniques
for non-linear approximate MIMO detectors with a low complexity
near-optimal performance is presented. The approach is particularly
attractive as Swarm Intelligence (SI) is well suited for physically
realizable, real- time applications, where low complexity and fast
convergence is of absolute importance. Application of Particle
Swarm Optimization (PSO) and Ant Colony Optimization (ACO)
algorithms is studied. While an optimal Maximum Likelihood (ML)
detection using an exhaustive search method is prohibitively
complex, it is established that Swarm Intelligence optimized MIMO
detection algorithms gives near-optimal Bit Error Rate (BER)
performance, thereby reducing the ML computational complexity
significantly
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