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The upper bound is new and general.
The lower bound is this, the upper bound is this.
Tightness of the derived upper bound is illustrated by Monte Carlo simulation results.
Subsequently, such an upper bound is minimized by properly designing the filter parameter recursively.
The performance upper bound is thus achieved.
Specifically, the upper bound is obtained via the following transformations.
where the upper bound is obtained for a uniform PDP.
Hence, in this case, our upper bound is asymptotically tight.
Therefore, the upper bound is maximized with maximum average power.
It is proved that the new upper bound is better than those in Theorems 1-3.
The simulation results show that the obtained upper bound is quite tight and reliable.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com