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The scheme proposed in [25] also uses PU stationary channel usage probabilities to rank the channels.
This simplification is made on the basis that it is the order in which links in the temporal reuse set are checked that determines the link usage probabilities.
Our scheme is different from the ones in the literature (e.g.,[23-25] ,[23-25] of using PU stationary channel usage probecauseies in deciding of the channel to be using
Temporal reuse-unaware C -optimised schedules with full rights extension (full temporal reuse) are analysed to estimate the usage probabilities U of links in temporal reuse sets based on their assigned priorities θ.
The average usage probabilities of 10-30 networksworks are given in Table 1 for networks operating with schedules that have no power-rate adaptivity, power adaptivity only, rate adaptivity only and both power and rate adaptivity.
The simplified usage probabilities of outgoing links in the temporal reuse set for a specific node n active in v k are given by U n ( v k ) = { β l : l ∈ O ( n ) } ⊆ U (10).
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The usage probability μcp(P u ) is obtained through simulations for different values of P u.
All three histograms depict the spectrum usage probability of PUs at different instances of time.
The usage probability vector U is estimated through simulation, and the process is explained and values given for networks with differing rate/power adaptivity.
The model can then be calibrated to real-world prices by multiplying Used(p i ) and Unused(p i ) by a cost factor, C. Setting k = 1.5, to ensure truthful submission of usage probability, this gives us the final options pricing model used by R&C [1]: OptionPrice ( p i ) = C 3 p i 2 4 (3).
The Hadamard (Schur) product of the usage probability estimates U ( v k ) and the rate template C v k ) give the resultant achievable capacity for v k with full temporal reuse, so the simplification of C e gives the estimated achievable rate region for networks with spatial and temporal reuse.
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