Range expansion of cooperative mobile wireless network based on swarm intelligence optimization

• Build cooperative transmission model to get the range expansion of the network.
• Presents two swarm intelligence algorithms to solve the unsolvable CT model.
• The advantage of IACA is high accuracy and GSO is time saving in our work.

We research maximum range expansion using a fixed number of mobile wireless nodes along a line which do distributed MIMO (Multiple Input Multiple Output) in diversity configuration, also known as cooperative transmission (CT). The emphasis is on optimal clusters and locations of these clusters to maximize the multi-hop reach along the line based on the swarm intelligence algorithms which are the Glowworm Swarm Optimization (GSO) and Improved Ant Colony Algorithm (IACA). We build the CT model which cannot be solved by conventional method, so we solve it by using GSO and IACA algorithms to get the optimal result. In GSO algorithm, every mobile wireless node is considered as a glowworm, and the intensity of signs is the intensity of luciferin. The direction of movement is determined as well as the direction of movement function under the constraint of outage probability in CT model. The contribution to the IACA is to modify the heuristic function and the pheromone update rule based on the optimization function to avoid local optimal result. Simulation experiments compared GSO, IACA with ACA and Exhaustive Attack Method (EAM), which proves GSO and IACA are effective, and the advantage of IACA is high accuracy and GSO is time saving.