For energy-intensive nodes, the duty cycle uses the optimal value to optimize the network lifetime; for those nodes with low energy consumption, the duty cycle is appropriately increased to reduce the transmission delay.
To improve the performance of this system, a Genetic Algorithm (GA) – as a well-known technique to solve the complex optimization problems – is also employed to optimize the network parameters including learning rate, momentum of the network and the number of MFs for each input.
Compared with non-cooperative protocols, cooperative protocols often try to optimize the network performance by solving complex optimization algorithms, such as MC-MAC [52], P-MAC [41], and DDMAC [44].
Different from the previous research, the previous optimization strategies choose to increase the energy efficiency of the node and optimize the network lifetime under a certain duty cycle.
In Section 4, we propose to optimize the network sum rate through a diagonal matrix W. The precoding vector optimization within the IA subspaces is presented in Section 5.
For MPSK modulation technique, we can also use UDNP to optimize the network lifetime.
Over the simulation time, femtocells change those values in order to optimize the network.
The algorithm searches for maximum k-valid matchings in order to optimize the network throughput.
We used to deploy the nodes into a non-equidistant method to optimize the network lifetime.
Therefore, propagation power can be used to optimize the network structure for restraining virus propagation.
Our results offer insight into the network deployment and protocol design that optimize the network lifetime.
