Optimization of Time-Hopping Sequences for Multi-Pulse Communication Based on Genetic Algorithm

In network communication systems, it is of great significance to avoid communication conflicts among nodes. Traditional time-slot control methods can solve this problem, yet they suffer from issues such as the increase in communication latency, the waste of communication resources, and the rise in the nodes energy consumption. This paper proposes to design the waveforms of communication data frames, aiming to create conditions for achieving low-latency and reliable communication among network nodes. A time-hopping pulse communication model is constructed, and the collision detection feature (CDF) and collision delay response (CDR) are defined to quantify collisions. An optimization problem is then formulated with the goal of minimizing conflicts among nodes. Since the objective function is non-convex, a genetic algorithm is used to solve it. Simulation results show that under different data scales, mutation rates, selection strategies, and population sizes, the genetic algorithm can effectively optimize the parameters of time-hopping sequences. It can effectively reduce the information loss caused by transmission-reception collisions. This is conducive to enhancing the stability of the system during the communication process and provides an effective way to achieve low-latency communication between nodes in the networking environment.