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Swarm intelligence based MIMO detection techniques in wireless systems

Swarm intelligence based MIMO detection techniques in wireless systems

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These nature-inspired techniques testified to be efficient maximum likelihood (ML) function optimizers. Their simple and less complex architectures make them worthy for non-deterministic polynomial (NP)-hard multi-input multi-output (MIMO) detection problem. Genetic algorithm, particle swarm optimization and ant colony optimization methods approach near optimal performance with significantly reduced computational complexity, particularly in the case of higher constellation systems and alphabet sizes with multiple transmitting antennas as compared to traditional ML detector that is computationally expensive and nonpractical to utilize. Swarm intelligence (SI) based mechanisms show their efficacy for solving MIMO detection problem as well as a promise for these heuristic algorithms to be applied in complex modulation mechanisms. One of the main contributions of the work in this chapter is to prove that SI is a useful optimization technique for classical communications issues for which these approaches were not considered very effective in the past.

Chapter Contents:

  • Abstract
  • 5.1 Introduction
  • 5.2 System model
  • 5.3 Problem formulation
  • 5.4 Existing MIMO detectors
  • 5.4.1 Linear detection
  • 5.4.1.1 Zero-forcing detection
  • 5.4.1.2 Minimum mean square error detection
  • 5.4.2 Nonlinear detectors
  • 5.4.2.1 Vertical Bell Labs Layered Space Time
  • 5.4.2.2 ML detector
  • 5.5 Nature-inspired optimization techniques
  • 5.5.1 Genetic algorithm
  • 5.5.2 Particle swarm optimization
  • 5.5.3 Ant colony optimization
  • 5.6 Genetic algorithm based detection for MIMO techniques
  • 5.6.1 Initialization of GA
  • 5.6.2 Fitness evaluation using cost function
  • 5.6.3 Optimality test
  • 5.6.4 Selection
  • 5.6.5 Reproduction
  • 5.6.6 Crossover
  • 5.6.7 Mutation
  • 5.6.8 Decision making
  • 5.6.9 Performance analysis
  • 5.6.9.1 BER performance analysis
  • 5.7 MIMO detection using particle swarm optimization
  • 5.7.1 PSO-MIMO detection algorithm
  • 5.7.2 SPSO-MIMO detection algorithm
  • 5.7.3 MPSO-MIMO detection algorithm
  • 5.7.4 MIMO detection algorithm based on binary PSO (BPSO-MIMO)
  • 5.7.5 Control of PSO parameters
  • 5.7.6 Performance analysis of PSO-based MIMO detection techniques
  • 5.7.6.1 Simulation setup
  • 5.7.7 MIMO-PSO detection algorithms'BER performance
  • 5.7.7.1 MIMO-SPSO and MPSO BER performance
  • 5.7.7.2 MIMO-SPSO and MPSO convergence patterns
  • 5.7.7.3 MIMO-BPSO performance
  • 5.7.7.4 Computational complexity theoretical evaluation
  • 5.7.8 Behavior with increase in iterations
  • 5.7.9 Effect of parameters adjustment in system behavior
  • 5.7.10 Analysis of MIMO-PSO as an effective MIMO detector
  • 5.8 MIMO detection using ant colony optimization (ACO) algorithm
  • 5.8.1 Binary ant system
  • 5.8.1.1 Solution construction
  • 5.8.1.2 Pheromone update
  • 5.8.2 BA-MIMO detection algorithm
  • 5.8.3 Performance evaluation of BA-MIMO detection
  • 5.8.4 Computational complexity comparison
  • 5.8.5 Performance complexity trade-off
  • 5.9 Applications of SI in MIMO detection
  • 5.10 Conclusion
  • References

Inspec keywords: genetic algorithms; particle swarm optimisation; antenna arrays; ant colony optimisation; maximum likelihood detection; transmitting antennas; computational complexity; MIMO communication

Other keywords: swarm intelligence based mechanisms; particle swarm optimization; reduced computational complexity; nondeterministic polynomial-hard multiinput multioutput detection problem; efficient maximum likelihood function optimizers; nature-inspired techniques; ant colony optimization methods approach; multiple transmitting antennas; MIMO detection problem; optimal performance; complex modulation mechanisms

Subjects: Signal detection; Antenna arrays; Other topics in statistics; Optimisation techniques; Radio links and equipment

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