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First, the minimum QoS and maximum interference constraints may not be simultaneously satisfied.
First, the target QoS constraints and the maximum interference constraints inflicted on the PUs are opposing ones and, thus, the feasible set may be void depending on the system parameters.
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Relays transmit over the primary spectrum under a maximum interference temperature1 constraint.
Constraint C1 represents the maximum allowed interference constraint imposed on the secondary users, and we obtained it by assuming equality in expression (8) and replacing (7) in the average interference constraint equation (6).
In this case, we consider the constrained rate maximization problem at the SU level in order to find the maximum achievable rate under interference constraints to PUs.
In (7), the first constraint implies the minimum rate required by EU i, the second set of constraints denotes the maximum interference levels regulated by each MD over every channel, and the last set of constraints imposes a total power constraint to each AP.
We propose a centralized algorithm with a greedy strategy that can minimize the maximum interference while satisfying the delay constraint.
We propose a PC algorithm under maximum transmit power constraints, SINR constraints and interference constraints to minimize the total BER for all SUs according to the actual situations.
The BER of the SUs with the spectrum sensing uncertainties is minimized under maximum transmit power constraints, SINR constraints, and interference constraints.
Then, we propose an optimal power allocation scheme in this scenario which holds the characteristics of achieving the maximum system utility and satisfying the QoS requirement of M2M devices and interference constraints of PU.
In order to solve the optimization problem, we can start by assuming that the maximum power that can be allocated for a given subcarrier is determined according to the interference constraints only by using (35 - 36 35 - 36ery set oforubcarrieveryor all.
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