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channels (i.e., same channel gain over all K channels for a given link is enforced).
As shown in Fig. 6, for a given link quality, the power consumption increases with a decreasing BER.
According to [18], for a given link condition, there exists a rate that maximizes performance (usually in terms of throughput).
This indicates that for a given link gain X, it obeys exponential distribution with hazard rate 1/ω X, denoted by X ~ Υ(1/ω X ).
The physical layer supports Adaptive Modulation and Coding (AMC), which is used to achieve the highest data rate for a given link quality.
The model includes retransmission statistics and shows that for a given link distance and number of channels used exist a single optimal radiation power level at which the mean energy consumption required to transmit a bit correctly is minimized.
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One such question regards the so-called link prediction problem, that is, predict for a future time window, whether a given link will appear or disappear from the network.
In this Appendix, we prove an expression (9) for the probability of transmission across a given link.
For any given link ℓ, there exists a subset of | I ≥ | of size at least | I ≥ | / 2 φ, in which any pair of links interferes with each other under the RTS/CTS model.
In this section, we employ a Markov chain model to calculate the expected completion time of S2HNC in the small M regime for given link erasure rates.
In Fig. 3, the convergence process when different values of Δ down are used is presented, for given link conditions whose parameters are listed in Table 2.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com