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We focus on the computational complexity reduction of the alternative BD methods.
Any computational complexity reduction mechanism can further reduce the computational cost and power consumption.
However, this significant computational complexity reduction is not obtained without price.
Costs and computational complexity reduction, improved performance and reduced chip size are just some of them.
This paper presents a method for the computational complexity reduction in a robust model predictive control.
To make the proposed method more feasible, the computational complexity reduction is the major direction of our future work.
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It runs algorithms optimized to make a trade-off between computational complexity, rate reduction and high selection efficiency.
WHoweverds, the supports of the generators modifiedlalgorithmse the recanery of the orientations of the generators is complexity theoretically intractabee in generapplied
This results in a reduction of computational complexity of complex-valued adaptive filters by a half, while providing enhanced physical insight and control over complex-valued estimation algorithms.
To validate the analytical results, extensive simulations have been conducted, showing that the proposed schemes can achieve significant reduction in computational complexity while keeping good PAPR reduction.
ABC -and PSO-optimized MIMO symbol detection mechanisms approach near-optimal performance with significant reduction in computational complexity, especially for complex systems with multiple transmitting antennas, where conventional ML detector is computationally expensive and impractical to deploy.
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