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Due to the limited bandwidth, only finite-level quantized sensor messages are sent to the fusion center, and multiple finite-level logarithmic quantizers are introduced to describe the above quantization strategy.
Therefore, the quantization strategy is the following.
The dynamic quantization strategy is employed, and stochastic variable is considered satisfying the Bernoulli distribution.
This superior coding performance is mainly owing to its novel quantization strategy.
The quantization strategy switches between "zooming out" and "zooming in" which holds two modes similarly to Sharon and Liberzon (2012).
Quantization strategy used here is a traditional uniform one, and Kalman filter is applied to obtain the estimated states.
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Yet, the quantization strategies differ, as we will review below.
These quantization strategies are denoted as correlated RCDQ.
Our conclusions hold also in other scenarios with different quantization strategies.
Two possible quantization strategies are considered at the sensors: (i) binary quantization and (ii) absence of quantization.
Figure 4 compares the sum capacity of the MIMO BC when the above quantization strategies are used.
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