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Therefore, only one state transition probability matrix, which is independent of the location of the receiver, may not accurately model the channels.
To study the impact of the channel condition variations on the system throughput and fairness performance, we model the channels through an 8-state Markov-Rayleigh fading channel model [30].
In the simulation, we model the channels as independent Rayleigh fading channels with unit average power gains, i.e., modeling the entries of (mathbf {H}^{n}_{ij}) as independent and identical (i.i.d).i.d
Simulations based on signal-to-interference-plus-noise ratio (SINR) losses [20], probability of detection (Pd), and probability of false alarm (Pfa) show the interest of our approach: LR filters and LR detectors which are obtained using AU-HOSVD outperform the vectorial approach and those obtained from HOSVD in the general polarimetry model (the channels HH and VV are not completely correlated).
Similar(56)
Crystal structures of two Sec61 homologues were used to model the channel.
Here, we model the channel efficiency with these basic steps.
We model the channel errors through Bit Error Rate (ber).
Given the channel transmission range classification, we can model the channel heterogeneity.
We use the Markov chain to model the channel state switching (see Figure 2).
The functions in (14) model the channel time behavior as polynomial in time.
Based on the proposed model, the channel and link availability time are predicted.
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Justyna Jupowicz-Kozak
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