Joint Optimization in Intelligent Reflecting Surface-Aided UAV Communication for Multiaccess Edge Computing

<table class="algorithm-group"><tr><td><table class="algorithm" id="alg1"><tr><td colspan="2"><b>Input:</b> initial variable</td></tr><tr><td colspan="2"><b>Output:</b> suboptimal solution</td></tr><tr><td colspan="2">(1) Randomly generate an initial population with a certain number of individuals</td></tr><tr><td colspan="2">(2) Use the fitness function to evaluate the population to determine whether the stopping condition is met; if so, stop and output the optimal solution; otherwise, continue to operate</td></tr><tr><td colspan="2">(3) Individuals that can be updated are selected according to their fitness. Individuals with high fitness have a high probability of being selected, and individuals with low fitness may be eliminated</td></tr><tr><td colspan="2">(4) Generate new individuals according to a certain crossover probability and method</td></tr><tr><td colspan="2">(5) Generate new individuals according to a certain mutation probability and method</td></tr><tr><td colspan="2">(6) Generate a new generation of population by crossover and mutation; return to step 2</td></tr></table></td></tr></table>

Wireless Communications and Mobile Computing

alg1

Algorithm 1

Algorithm 1: Joint Optimization in Intelligent Reflecting Surface-Aided UAV Communication for Multiaccess Edge Computing 