Optimal Routing and Traffic Scheduling for Multihop Cellular Networks Using Genetic Algorithm

When considering a multicell scenario with nonuniform traffic distribution in multihop wireless networks, the search for the optimum topology becomes an NP-hard problem. For such problems, exact algorithms based on exhaustive search are only useful for small toy models, so heuristic algorithms such as genetic algorithms (GA) must be used in practice. For this purpose, we present a novel sequential genetic algorithm (SGA) to optimize the relaying topology in multihop cellular networks aware of the intercell interference and the spatial traffic distribution dynamics. We encode the topologies as a set of chromosomes and special crossover and mutation operations are proposed to search for the optimum topology. The performance is measured by a fitness function that includes the throughput, power consumption and delay. Improvement in the fitness function is sequentially controlled as newer generations evolve and whenever the improvement is sufficiently increased the current topology is updated by the new one having higher fitness. Numerical results show that SGA provides both high performance improvements in the system and fast convergence (at least one order of magnitude faster than exhaustive search) in a dynamic network environment. We also demonstrate the robustness of our algorithm to the initial state of the network.