A Bioinspired Scheduling Strategy for Dense Wireless Networks Under the SINR Model

Abstract

Novel wireless networking technologies such as massive Internet-of-Things and 5G-and-beyond cellular networks are becoming increasingly denser. The SINR model can improve the performance of dense wireless networks by taking into consideration the effects of interference to allow multiple simultaneous transmissions in the same coverage area. However, transmission scheduling under the SINR model is an NP-hard problem. This work presents a bioinspired solution based on a genetic heuristic. The Genetic-based Transmission Scheduler (GeTS) produces efficient transmission schedules, increasing the number of simultaneous transmissions (i.e., spatial reuse). Simulation results are presented, including a convergence test and a comparison with the optimal algorithm and another heuristic.