The advantage of using simple random sampling is that it allows all elements of the entire population (in our case, those represent all service organizations registered with ASE) to have equal selection probability, that is, all elements within the population are equally likely to be selected (Saunders et al. 2012).
We assume that the validation sample is drawn from the primary sample by the Bernoulli sampling with equal selection probability.
They are equal selection and interference weighted selection policies, and we drive their outage probabilities for a macrocell user.
The interference weighted selection policy achieves lower outage probability and higher spectrum efficiency than the equal selection policy.
If S F 1 < S m q, we do not need to apply the equal selection policy.
With the equal selection policy, MUE need to inform all HeNBs in F 1 of S F 1 through the backhaul network.
In the equal selection policy, we define the sharing probability p s E as p s E = min 1, S m q S F 1. (10).
The | K s | of the decentralized schemes which are the equal selection policy, marked as 'Decentralized Equal', and the interference weighted selection policy, marked as 'Decentralized Weight' are is nearly comparable to that in the centralized one.
The mean and variance of S m j with the equal selection policy is scaled by n F j p s E and n F j ( p s E ) 2, respectively, where n F j is the number of HeNBs in F 1 j, i.e., | F 1 j |.
In this section, we consider two types of spectrum sharing probability; one is the equal selection probability p s E, and the other the interference weighted selection probability p s W. Then we compare their performance in terms of outage probability.
Because this policy gives the spectrum sharing opportunity equally to all HeNBs in F 1, the resultant cross-tier interference at MUE with the equal selection policy is given by S m : = p s E ∑ i ∈ F 1 I i. (11).
