Successive Interference Cancellation Algorithm based on Co-Evolutionary Power Control

In order to achieve successive interference cancellation in wireless networks to improve network throughput, it is particularly important to control the transmit power of wireless nodes. Traditional network, genetic algorithm (GA) is prone to phenomena such as premature and falling into local optimum when solving power control problems. Therefore, we propose a co-evolutionary algorithm based on genetic algorithm (COGA). This method defines the fitness value of the individual, divides the fitness value into three equal parts in descending order, and puts the individuals into three different subgroups on average. During the evolution process, these three sub-populations co-evolve with each other, which not only ensures that the population moves in the direction of the optimal solution, but also maintains the diversity of the population. Experimental results show that this method effectively improves the convergence speed of power control and can obtain higher wireless network throughput.